From d1679539ddb149aa7e92400a9242a9e8ed822a6a Mon Sep 17 00:00:00 2001 From: Eric Harper Date: Wed, 24 Aug 2022 14:18:02 -0600 Subject: [PATCH] Merge r1.11.0 main (#4787) * NeMo Megatron doc updates Signed-off-by: Oleksii Kuchaiev * update branch Signed-off-by: ericharper * update package info and dockerfile Signed-off-by: ericharper * fix fastpitch export (#4676) Signed-off-by: Jason * [TTS] fixed wrong pronunciations for r1.11. (#4677) * [TTS] fixed wrong pronunciations. Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * incremented the version number to 22.08 as @blisc suggested. Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * correct cmudict versions in world-wide places. Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Fix for incorrect batch size issue while decoding (#4675) Co-authored-by: Micha Livne Co-authored-by: Eric Harper * [TTS] incremented the version number to 22.08 in tutorials. (#4684) * [TTS] incremented the version number to 22.08 in tutorials. Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Megatron encode function with RPE fix (#4692) * Fix for RPE Signed-off-by: MaximumEntropy * Style Signed-off-by: MaximumEntropy * fix to fetch config file (#4699) Signed-off-by: nithinraok * Fix notebook for buffered inference (#4703) Signed-off-by: smajumdar * Prompt Learning Notebook Bug Fix (#4689) * Added back dataset class list of dict input for generation in tutorial notebook Signed-off-by: Virginia Adams * updated argument name for build dataset Signed-off-by: Virginia Adams Signed-off-by: Virginia Adams * add psutils to mock imports (#4728) Signed-off-by: ericharper Signed-off-by: ericharper * Update Aligner model and tutorial to add NGC checkpoint loading (#4714) * Update Aligner model and tutorial to add NGC checkpoint loading Signed-off-by: Jocelyn Huang * Fix pynini install for Aligner notebook, minor formatting fix for model Signed-off-by: Jocelyn Huang * Aligner notebook formatting consistency Signed-off-by: Jocelyn Huang Signed-off-by: Jocelyn Huang Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * [TTS] bugfix for missing configs. (#4725) Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * docs typo fix Signed-off-by: Oleksii Kuchaiev * Fix pynini install in TTS tutorials (#4729) Signed-off-by: Jocelyn Huang Signed-off-by: Jocelyn Huang * Fix ASR notebooks (#4738) Signed-off-by: smajumdar Signed-off-by: smajumdar * Multilingual VAD model (#4734) * add ngc link Signed-off-by: fayejf * add tuned VAD config on ASR data Signed-off-by: fayejf * yaml note Signed-off-by: fayejf * update vad asr notebook with mVAD Signed-off-by: fayejf * update vad infer config comment Signed-off-by: fayejf * fix Signed-off-by: fayejf * mvad sd config for ch109 Signed-off-by: fayejf * update sd readme Signed-off-by: fayejf * add new mVAD model to doc Signed-off-by: fayejf * style fix Signed-off-by: fayejf * update sd tutorial with mVAD Signed-off-by: fayejf * typo fix Signed-off-by: fayejf Signed-off-by: fayejf * publish pretrained itn t5 model for English (#4748) Signed-off-by: Alexandra Antonova Signed-off-by: Alexandra Antonova Co-authored-by: Alexandra Antonova * Updated docs and doc paths (#4754) * Updated docs and doc paths Signed-off-by: Virginia Adams * Update Multitask_Prompt_and_PTuning.ipynb * Update README.rst * Changed branch name to use single quotes Signed-off-by: Virginia Adams Signed-off-by: Virginia Adams * fix bug relating to ddp strategy in joint intent slot classification tutorial (#4762) * [TTS] updated config with a German IPA phoneme tokenizer (#4756) * [TTS] added a German IPA phoneme tokenizer * [TTS][ASR] enabled customized arguments for trimming the leading and trailing silence. * [TTS] disabled spline interpolation for beta-binomial distribution. Let it generate align prior and save to disks. Use a new phoneme tokenizer. * [TTS] use consistent spline interpolation with fastpitch checkpoint when generating mel-spectrograms for hifigan finetune. Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Update r1.11 to new heteronyms list (#4745) * Update configs to new heteronyms list * Remove old heteronyms list, add alt 'merchandise' pron to CMUdict * Update remaining references to old heteronyms list Signed-off-by: Jocelyn Huang Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * [TTS] Add multi-speaker German FastPitch and HiFiGAN NGC checkpoints (#4763) Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * [TTS] Add single male speaker German FastPitch and HiFiGAN NGC checkpoints (#4770) Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Update CMUdict with more recent 0.7b entries (#4768) Signed-off-by: Jocelyn Huang Signed-off-by: Jocelyn Huang Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * Install pynini in docker container (#4733) Signed-off-by: Vladimir Bataev Signed-off-by: Vladimir Bataev Co-authored-by: Nithin Rao Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Eric Harper * Fix tutorial formatting (#4778) Signed-off-by: Jocelyn Huang * [TTS] deprecated old scripts for ljspeech. (#4780) * deprecated old scripts for ljspeech. * removed relevent function calls in TTS docs. Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> * update branch Signed-off-by: ericharper * update package info and requirements Signed-off-by: ericharper * update container Signed-off-by: ericharper * Update stragglers to new cmudict and heteronyms paths Signed-off-by: Jocelyn Huang Signed-off-by: Oleksii Kuchaiev Signed-off-by: ericharper Signed-off-by: Jason Signed-off-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Signed-off-by: MaximumEntropy Signed-off-by: nithinraok Signed-off-by: smajumdar Signed-off-by: Virginia Adams Signed-off-by: Jocelyn Huang Signed-off-by: fayejf Signed-off-by: Alexandra Antonova Signed-off-by: Vladimir Bataev Co-authored-by: Oleksii Kuchaiev Co-authored-by: Jason Co-authored-by: Xuesong Yang <1646669+XuesongYang@users.noreply.github.com> Co-authored-by: Rajesh Ilango Co-authored-by: Micha Livne Co-authored-by: Sandeep Subramanian Co-authored-by: Nithin Rao Co-authored-by: Somshubra Majumdar Co-authored-by: Virginia Adams <78445382+vadam5@users.noreply.github.com> Co-authored-by: Jocelyn Co-authored-by: fayejf <36722593+fayejf@users.noreply.github.com> Co-authored-by: bene-ges <61418381+bene-ges@users.noreply.github.com> Co-authored-by: Alexandra Antonova Co-authored-by: Zhilin Wang Co-authored-by: Vladimir Bataev Signed-off-by: Hainan Xu --- Dockerfile | 10 +- Jenkinsfile | 2 +- README.rst | 12 +- .../data/diarization_results.csv | 1 + .../asr/speaker_diarization/results.rst | 4 +- .../data/classification_results.csv | 1 + docs/source/conf.py | 1 + .../nlp/nemo_megatron/prompt_learning.rst | 116 +-- docs/source/tts/datasets.rst | 179 +---- .../vad/vad_inference_postprocessing.yaml | 26 +- .../megatron_gpt_prompt_learning_eval.py | 2 +- examples/speaker_tasks/diarization/README.md | 9 +- .../diarization/conf/offline_diarization.yaml | 12 +- .../conf/offline_diarization_with_asr.yaml | 12 +- examples/tts/conf/aligner.yaml | 2 +- .../tts/conf/de/fastpitch_align_44100.yaml | 5 +- examples/tts/conf/fastpitch_align_44100.yaml | 4 +- examples/tts/conf/fastpitch_align_ipa.yaml | 2 +- examples/tts/conf/fastpitch_align_v1.05.yaml | 4 +- examples/tts/conf/mixer-tts.yaml | 4 +- examples/tts/conf/rad-tts_dec.yaml | 2 +- examples/tts/conf/rad-tts_feature_pred.yaml | 2 +- examples/tts/conf/tacotron2.yaml | 4 +- .../asr/models/classification_models.py | 7 + .../asr/parts/utils/transcribe_utils.py | 4 +- .../megatron/gpt_prompt_learning_dataset.py | 14 +- .../duplex_decoder.py | 7 + .../duplex_tagger.py | 7 + .../megatron_gpt_prompt_learning_model.py | 19 +- .../megatron_lm_encoder_decoder_model.py | 12 +- .../megatron/token_level_encoder_decoder.py | 6 +- nemo/collections/tts/models/aligner.py | 24 +- nemo/collections/tts/models/fastpitch.py | 35 +- nemo/collections/tts/models/hifigan.py | 22 + nemo/collections/tts/torch/tts_dataset.yaml | 4 +- nemo/package_info.py | 2 +- scripts/dataset_processing/ljspeech/README | 16 - .../ljspeech/calculate_durs.py | 126 ---- .../create_manifests_and_textfiles.py | 130 ---- .../ljspeech/create_token2idx_dict.py | 68 -- .../ljspeech/extract_ljspeech_energy_pitch.py | 88 --- .../extract_ljspeech_phonemes_and_durs.sh | 150 ---- .../ljspeech/get_lj_speech_data.py | 71 -- .../tts/extract_sup_data.py | 5 +- .../ds_conf/ds_for_fastpitch_align.yaml | 3 +- .../ds_conf/ds_for_fastpitch_align.yaml | 4 +- .../ljspeech/ds_conf/ds_for_mixer_tts.yaml | 4 +- ...dict-0.7b_nv22.07 => cmudict-0.7b_nv22.08} | 703 +++++++++++++++++- scripts/tts_dataset_files/heteronyms-030921 | 413 ---------- ...22.06.txt => ipa_cmudict-0.7b_nv22.08.txt} | 5 +- tests/collections/nlp/test_prompt_learning.py | 2 +- .../asr/Buffered_Transducer_Inference.ipynb | 4 +- tutorials/asr/Offline_ASR.ipynb | 2 +- .../Offline_ASR_with_VAD_for_CTC_models.ipynb | 8 +- ...Joint_Intent_and_Slot_Classification.ipynb | 8 +- .../nlp/Multitask_Prompt_and_PTuning.ipynb | 4 +- .../Speaker_Diarization_Inference.ipynb | 4 +- .../Speaker_Identification_Verification.ipynb | 5 +- .../tts/Aligner_Inference_Examples.ipynb | 14 +- tutorials/tts/FastPitch_Finetuning.ipynb | 18 +- ...ynb => FastPitch_GermanTTS_Training.ipynb} | 75 +- .../tts/FastPitch_MixerTTS_Training.ipynb | 12 +- .../tts/Inference_DurationPitchControl.ipynb | 6 +- tutorials/tts/Inference_ModelSelect.ipynb | 6 +- tutorials/tts/Tacotron2_Training.ipynb | 12 +- 65 files changed, 1077 insertions(+), 1468 deletions(-) delete mode 100644 scripts/dataset_processing/ljspeech/README delete mode 100644 scripts/dataset_processing/ljspeech/calculate_durs.py delete mode 100644 scripts/dataset_processing/ljspeech/create_manifests_and_textfiles.py delete mode 100644 scripts/dataset_processing/ljspeech/create_token2idx_dict.py delete mode 100644 scripts/dataset_processing/ljspeech/extract_ljspeech_energy_pitch.py delete mode 100755 scripts/dataset_processing/ljspeech/extract_ljspeech_phonemes_and_durs.sh delete mode 100644 scripts/dataset_processing/ljspeech/get_lj_speech_data.py rename scripts/tts_dataset_files/{cmudict-0.7b_nv22.07 => cmudict-0.7b_nv22.08} (99%) delete mode 100644 scripts/tts_dataset_files/heteronyms-030921 rename scripts/tts_dataset_files/{ipa_cmudict-0.7b_nv22.06.txt => ipa_cmudict-0.7b_nv22.08.txt} (99%) rename tutorials/tts/{Fastpitch_Training_GermanTTS.ipynb => FastPitch_GermanTTS_Training.ipynb} (95%) diff --git a/Dockerfile b/Dockerfile index ca4edd1e850f..49662bab1ea2 100644 --- a/Dockerfile +++ b/Dockerfile @@ -34,9 +34,9 @@ RUN apt-get update && \ # FIXME a workaround to update apex. Remove when base image is updated WORKDIR /tmp/ -RUN git clone https://github.com/ericharper/apex.git && \ +RUN git clone https://github.com/NVIDIA/apex.git && \ cd apex && \ - git checkout 19e4f55eb402452f74dead19f68b65d6291cfdb2 && \ + git checkout 3c19f1061879394f28272a99a7ea26d58f72dace && \ pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" ./ # uninstall stuff from base container @@ -60,6 +60,10 @@ WORKDIR /tmp/nemo COPY requirements . RUN for f in $(ls requirements*.txt); do pip install --disable-pip-version-check --no-cache-dir -r $f; done +# install pynini +COPY nemo_text_processing/install_pynini.sh /tmp/nemo/ +RUN /bin/bash /tmp/nemo/install_pynini.sh + # install k2, skip if installation fails COPY scripts /tmp/nemo/scripts/ RUN /bin/bash /tmp/nemo/scripts/speech_recognition/k2/setup.sh || exit 0 @@ -70,7 +74,7 @@ COPY . . # start building the final container FROM nemo-deps as nemo -ARG NEMO_VERSION=1.11.0 +ARG NEMO_VERSION=1.12.0 # Check that NEMO_VERSION is set. Build will fail without this. Expose NEMO and base container # version information as runtime environment variable for introspection purposes diff --git a/Jenkinsfile b/Jenkinsfile index ba19180bcb5f..fc14a225a8cd 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -2,7 +2,7 @@ pipeline { agent { docker { //image 'nvcr.io/nvidia/pytorch:22.05-py3' - image 'gitlab-master.nvidia.com:5005/eharper/nemo_containers:megatron_gpt_v16' + image 'gitlab-master.nvidia.com:5005/eharper/nemo_containers:nemo_ci_pytorch_22.07_apex_3c19f1061879394f28272a99a7ea26d58f72dace' args '--device=/dev/nvidia0 --gpus all -e TRANSFORMERS_OFFLINE=1 --user 0:128 -v /home/TestData:/home/TestData -v $HOME/.cache:/root/.cache --shm-size=8g' } } diff --git a/README.rst b/README.rst index 88b59e86fb16..272dd6cef024 100644 --- a/README.rst +++ b/README.rst @@ -68,7 +68,7 @@ Key Features * `Information retrieval `_ * `Entity Linking `_ * `Dialogue State Tracking `_ - * `Prompt Learning `_ + * `Prompt Learning `_ * `NGC collection of pre-trained NLP models. `_ * `Speech synthesis (TTS) `_ * Spectrogram generation: Tacotron2, GlowTTS, TalkNet, FastPitch, FastSpeech2, Mixer-TTS, Mixer-TTS-X @@ -205,6 +205,12 @@ Megatron GPT training requires NVIDIA Apex to be installed. git checkout 3c19f1061879394f28272a99a7ea26d58f72dace pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" ./ +.. note:: + + You may need to modify [setup.py](https://github.com/NVIDIA/apex/blob/3c19f1061879394f28272a99a7ea26d58f72dace/setup.py) if + your version of CUDA does not match the version used to compile Pytorch binaries, comment lines 33-41 in the above link + before installing. + Docker containers: ~~~~~~~~~~~~~~~~~~ To build a nemo container with Dockerfile from a branch, please run @@ -214,13 +220,13 @@ To build a nemo container with Dockerfile from a branch, please run DOCKER_BUILDKIT=1 docker build -f Dockerfile -t nemo:latest . -If you chose to work with main branch, we recommend using NVIDIA's PyTorch container version 22.05-py3 and then installing from GitHub. +If you chose to work with main branch, we recommend using NVIDIA's PyTorch container version 22.07-py3 and then installing from GitHub. .. code-block:: bash docker run --gpus all -it --rm -v :/NeMo --shm-size=8g \ -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \ - stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:22.05-py3 + stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:22.07-py3 Examples -------- diff --git a/docs/source/asr/speaker_diarization/data/diarization_results.csv b/docs/source/asr/speaker_diarization/data/diarization_results.csv index 703bbb4c9063..c71345ac3dc7 100644 --- a/docs/source/asr/speaker_diarization/data/diarization_results.csv +++ b/docs/source/asr/speaker_diarization/data/diarization_results.csv @@ -1,4 +1,5 @@ Model Name,Model Base Class,Model Card +vad_multilingual_marblenet,EncDecClassificationModel,"https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet" vad_marblenet,EncDecClassificationModel,"https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet" vad_telephony_marblenet,EncDecClassificationModel,"https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet" titanet_large,EncDecSpeakerLabelModel,"https://ngc.nvidia.com/catalog/models/nvidia:nemo:titanet_large" diff --git a/docs/source/asr/speaker_diarization/results.rst b/docs/source/asr/speaker_diarization/results.rst index 9aa231e5bfc9..4fa3ab700334 100644 --- a/docs/source/asr/speaker_diarization/results.rst +++ b/docs/source/asr/speaker_diarization/results.rst @@ -11,7 +11,7 @@ Load VAD model .. code-block:: bash - pretrained_vad_model='/path/to/vad_marblenet.nemo' # local .nemo or pretrained vad model name + pretrained_vad_model='/path/to/vad_multilingual_marblenet.nemo' # local .nemo or pretrained vad model name ... # pass with hydra config config.diarizer.vad.model_path=pretrained_vad_model @@ -58,7 +58,7 @@ In general, you can load models with model name in the following format, .. code-block:: python - pretrained_vad_model='vad_telephony_marblenet' + pretrained_vad_model='vad_multilingual_marblenet' pretrained_speaker_model='titanet_large' ... config.diarizer.vad.model_path=retrained_vad_model \ diff --git a/docs/source/asr/speech_classification/data/classification_results.csv b/docs/source/asr/speech_classification/data/classification_results.csv index 25e3c844a489..1b6033473b3d 100644 --- a/docs/source/asr/speech_classification/data/classification_results.csv +++ b/docs/source/asr/speech_classification/data/classification_results.csv @@ -1,4 +1,5 @@ Model Name,Model Base Class,Model Card +vad_multilingual_marblenet,EncDecClassificationModel,"https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet" vad_marblenet,EncDecClassificationModel,"https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet" vad_telephony_marblenet,EncDecClassificationModel,"https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet" commandrecognition_en_matchboxnet3x1x64_v1,EncDecClassificationModel,"https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v1" diff --git a/docs/source/conf.py b/docs/source/conf.py index 681588f544a8..8164f231a5d6 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -58,6 +58,7 @@ 'joblib', 'IPython', 'ipadic', + 'psutil', ] _skipped_autodoc_mock_imports = ['wrapt', 'numpy'] diff --git a/docs/source/nlp/nemo_megatron/prompt_learning.rst b/docs/source/nlp/nemo_megatron/prompt_learning.rst index 8ad01288c948..c0cca1203668 100644 --- a/docs/source/nlp/nemo_megatron/prompt_learning.rst +++ b/docs/source/nlp/nemo_megatron/prompt_learning.rst @@ -5,7 +5,7 @@ Prompt Learning Within NeMo we refer to **p-tuning** and **prompt tuning** methods collectively as prompt learning. Both methods are parameter efficient alternatives to fine-tuning pretrained language models. Our NeMo implementation makes it possible to use one pretrained GPT model on many downstream tasks without needing to tune the model's full set of parameters. It also allows for adding new tasks to your model without overwriting or disrupting previous tasks for which the model has already been p-tuned/prompt-tuned. Because the original model parameters are frozen and never altered by either method, p-tuning/prompt-tuning also avoids catastrophic forgetting issues often encountered when fine-tuning models. -Instead of selecting discrete text prompts in a manual or automated fashion, prompt tuning and p-tuning utilize virtual prompt embeddings that can be optimized via gradient decent. The only difference between prompt tuning and p-tuning within NeMo-Megatron is the architecture used to tune the soft prompt tokens during training. +Instead of selecting discrete text prompts in a manual or automated fashion, prompt tuning and p-tuning utilize virtual prompt embeddings that can be optimized via gradient descent. The only difference between prompt tuning and p-tuning within NeMo-Megatron is the architecture used to tune the soft prompt tokens during training. - Our prompt tuning implementation is based off Lester et. al’s EMNLP 2021 paper "`The Power of Scale for Parameter-Efficient Prompt Tuning `_" - Our p-tuning implementation is based off Liu et al's paper "`GPT Understands, Too `_" @@ -217,13 +217,12 @@ First define a config called ``multitask-prompt-learning.yaml`` demonstrated bel model: seed: 1234 nemo_path: ${name}.nemo - lm_finetune: False - pseudo_token_base: "PROMPT_" virtual_prompt_style: "prompt-tuning" encoder_seq_length: 2048 tensor_model_parallel_size: 1 pipeline_model_parallel_size: 1 - batch_size: 8 + global_batch_size: 16 + micro_batch_size: 4 restore_path: null language_model_path: models/megatron_125M_gpt.nemo @@ -281,58 +280,57 @@ In this example, the SQuAD task includes the question context as part of the pro trainer: ... exp_manager: ... model: - seed: 1234 - nemo_path: ${name}.nemo - lm_finetune: False - pseudo_token_base: "PROMPT_" - virtual_prompt_style: "p-tuning" # *** - encoder_seq_length: 2048 - tensor_model_parallel_size: 1 - pipeline_model_parallel_size: 1 - batch_size: 8 - - restore_path: multitask_prompt_tuning.nemo # *** - language_model_path: models/megatron_125M_gpt.nemo - existing_tasks: ["sentiment", "intent_and_slot"] # *** - new_tasks: ["squad"] - - task_templates: - - taskname: "sentiment" - prompt_template: "<|VIRTUAL_PROMPT_0|> {sentence} sentiment: {label}" - total_virtual_tokens: 100 - virtual_token_splits: [100] - truncate_field: null - answer_only_loss: False - - - taskname: "intent_and_slot" - prompt_template: "<|VIRTUAL_PROMPT_0|> Predict intent and slot <|VIRTUAL_PROMPT_1|> :\n{utterance}{label}" - total_virtual_tokens: 100 - virtual_token_splits: [80, 20] - truncate_field: null - answer_only_loss: True - answer_field: "label" - - - taskname: "squad" # *** - prompt_template: "<|VIRTUAL_PROMPT_0|> Answer the question from the context {question} {context} Answer: {answer}" # *** - total_virtual_tokens: 9 # *** - virtual_token_splits: [9] # *** - truncate_field: context # *** - answer_only_loss: True # *** - answer_field: "answer" # *** - - p_tuning: # *** - dropout: 0.0 # *** - num_layers: 2 # *** - - data: - train_ds: ["data/squad_train.jsonl"] # *** - validation_ds: ["data/squad_val.jsonl"] # *** - add_eos: True - shuffle: True - num_workers: 1 - pin_memory: True - - optim: ... + seed: 1234 + nemo_path: ${name}.nemo + virtual_prompt_style: "p-tuning" # *** + encoder_seq_length: 2048 + tensor_model_parallel_size: 1 + pipeline_model_parallel_size: 1 + global_batch_size: 16 + micro_batch_size: 4 + + restore_path: multitask_prompt_tuning.nemo # *** + language_model_path: models/megatron_125M_gpt.nemo + existing_tasks: ["sentiment", "intent_and_slot"] # *** + new_tasks: ["squad"] + + task_templates: + - taskname: "sentiment" + prompt_template: "<|VIRTUAL_PROMPT_0|> {sentence} sentiment: {label}" + total_virtual_tokens: 100 + virtual_token_splits: [100] + truncate_field: null + answer_only_loss: False + + - taskname: "intent_and_slot" + prompt_template: "<|VIRTUAL_PROMPT_0|> Predict intent and slot <|VIRTUAL_PROMPT_1|> :\n{utterance}{label}" + total_virtual_tokens: 100 + virtual_token_splits: [80, 20] + truncate_field: null + answer_only_loss: True + answer_field: "label" + + - taskname: "squad" # *** + prompt_template: "<|VIRTUAL_PROMPT_0|> Answer the question from the context {question} {context} Answer: {answer}" # *** + total_virtual_tokens: 9 # *** + virtual_token_splits: [9] # *** + truncate_field: context # *** + answer_only_loss: True # *** + answer_field: "answer" # *** + + p_tuning: # *** + dropout: 0.0 # *** + num_layers: 2 # *** + + data: + train_ds: ["data/squad_train.jsonl"] # *** + validation_ds: ["data/squad_val.jsonl"] # *** + add_eos: True + shuffle: True + num_workers: 1 + pin_memory: True + + optim: ... Then run the command again: @@ -356,7 +354,7 @@ The inference file can contain a mix of prompts from all the tasks the model has trainer.num_nodes=1 \ tensor_model_parallel_size=1 \ pipeline_model_parallel_size=1 \ - data_paths=[path/to/dataset1.jsonl, path/to/dataset2.jsonl] + prompts=[prompt1,prompt2] ``virtual_prompt_model_file`` should be a path to a .nemo file saved after p-tuning/prompt tuning and ``model_file`` is still the path to the gpt model's .nemo file. @@ -384,7 +382,9 @@ And the dataset class will automatically format your input to have the form: '<|VIRTUAL_PROMPT_0|> Context: some paragraph Question: question related to paragraph Answer: ', '<|VIRTUAL_PROMPT_0|> Context: another paragraph Question: a different question related to paragraph Answer: ' ] + +Generally prompt learning inference is just like running inference with a GPT model. The only difference is you need to add ``virtual_prompt_model_file=PATH_TO_NEMO_PROMPT_LEARNING_MODEL_FILE`` to your command if you're using a p-tuned/prompt-tuned model. Example prompt learning script: `NeMo/examples/nlp/language_modeling/megatron_gpt_prompt_learning.py.py `__. -Example prompt tuned inference script: `NeMo/examples/nlp/language_modeling/megatron_gpt_prompt_learning_eval.py `__. +Example prompt tuned inference script: `NeMo/examples/nlp/language_modeling/megatron_gpt_eval.py `__. diff --git a/docs/source/tts/datasets.rst b/docs/source/tts/datasets.rst index 959e6159b9fa..32d337370616 100644 --- a/docs/source/tts/datasets.rst +++ b/docs/source/tts/datasets.rst @@ -6,182 +6,5 @@ However, some models may require supplementary data for training and validation. The following sections contain details of required data formats and instructions for running preprocessing scripts for such models. +.. note:: To be updated. -FastSpeech 2 ------------- - -The FastSpeech 2 model converts from phonemes to Mel Spectrograms and predicts phoneme durations, pitches, and -energies. -Therefore, in addition to the manifest and audio samples, it requires some supplementary files and data: - -* A **mappings file** for converting from words to phonemes and phonemes to indices -* **Phoneme durations** (for each audio sample) -* **Pitch per frame** (for each training audio sample) -* **Energy per frame** (for each training audio sample) -* (Optional) **Ignore list** for filtering out samples with OOV words - -The ``FastSpeech2Dataset`` uses the manifest format shared with all other NeMo speech tasks. -Each line of the manifest should describe one sample, and should be in the following format: - -.. code:: - - {"audio_filepath": "/data/wavs/audio.wav", "text": "an unused transcription", "duration": 23.147} - -See the documentation on :ref:`Preparing Custom ASR Data` for more details on the NeMo speech manifest format. - -.. note:: - The ``FastSpeech2Dataset`` ignores the ``"text"`` field of the manifest, since the model reads phoneme indices from - the supplementary duration files instead. - -The **mappings file** should be a JSON-formatted file that contains dictionaries ``word2phones`` and ``phone2idx``. -``word2phones`` is a mapping that converts from your vocabulary to phonemes. -For example, one entry in ``word2phones`` might be ``"nemo": ["N", "EH1", "M", "OW0"]``. -``phone2idx`` is an (arbitrary) mapping that converts each phoneme to an index for prediction. -It is also used to determine the ``pad_id``, which is set to the length of ``phone2idx``. - -Together, these two dictionaries are also used for inference, as an input sentence is first normalized, then converted -to phonemes using ``word2phones``, and then converted from phonemes to indices using ``phone2idx``. - -For each audio sample, there should be a corresponding **phoneme durations** file that contains ``token_duration`` -(duration per phoneme in frames) and ``text_encoded`` (index of each phoneme corresponding to ``phone2idx``). - -For each sample, there should also be a **pitch file** that contains the pitches (F0) per frame, and an **energy file** -that contains the energies (L2-norm of STFT frame amplitudes) per frame. - -The **ignore list** is a pickled list of file base names (with no extension) that tells the ``FastSpeech2Dataset`` -which audio samples to discard. -You should use this to exclude samples from your manifest that contain OOV words. - - -Directory Structure -^^^^^^^^^^^^^^^^^^^ - -While the mappings file and ignore list paths are passed in to the ``FastSpeech2Dataset`` directly, the -``FastSpeech2Dataset`` infers the path to the supplementary files corresponding to each audio sample based on the -audio samples' paths found in the manifest. -For each sample, the paths to the corresponding duration, pitch, and energy files are inferred by replacing ``wavs/`` -with ``phoneme_durations/``, ``pitches/``, and ``energies``, and swapping out the file extension (``.wav``) with -``.pt``, ``.npy``, and ``.npy`` respectively. - -For example, given manifest audio path ``/data/LJSpeech/wavs/LJ001-0001.wav``, the inferred duration and phonemes file -path would be ``/data/LJSpeech/phoneme_durations/LJ001-0001.pt``. - -Your directory structure should therefore look something like this: - -.. code:: - - data/ - ├── manifest.json - ├── mappings.json - ├── ignore_file.pkl - ├── energies/ - │   ├── basename_1.npy - │   ├── basename_2.npy - │   ├── ... - │   └── basename_n.npy - ├── phoneme_durations/ - │   ├── basename_1.pt - │   ├── basename_2.pt - │   ├── ... - │   └── basename_n.pt - ├── pitches/ - │   ├── basename_1.npy - │   ├── basename_2.npy - │   ├── ... - │   └── basename_n.npy - └── wavs/ -    ├── basename_1.wav -    ├── basename_2.wav -    ├── ... -    └── basename_n.wav - - -LJ Speech Dataset -^^^^^^^^^^^^^^^^^ - -NeMo comes with scripts for preprocessing the LJ Speech dataset for use with FastSpeech 2, which can be found in the -``/scripts/dataset_processing/ljspeech/`` directory. -These scripts assume that you have downloaded the LJ Speech dataset and extracted it to ````. - -They perform the following dataset preparation steps: - -* Create manifest files and ``.txt`` files with normalized transcripts -* Extract pitches -* Extract energies -* Downloads the `CMU Pronouncing Dictionary `_ to convert normalized - transcripts to phonemes -* Uses the `Montreal Forced Aligner `_ (MFA) to - perform alignment -* Calculates durations based on the alignment -* Generates the mappings file and ignore file for the dataset - -To run the scripts, follow the steps below. - -#. Download and extract the `LJ Speech dataset `_ to ````. - -#. Create the manifest files and normalized text files (for MFA to discover later) by running: - - .. code-block:: bash - - python create_manifests_and_textfiles.py --ljspeech_base= --normalizer_class=ENCharParser --save_transcripts_in_txt --manifest_text_var_is_normalized - -#. Extract pitches and energies from the audio files: - - .. code-block:: bash - - python extract_ljspeech_energy_pitch.py --ljspeech_base= - - This will write the extracted pitches and energies to ``/pitches/`` and - ``/energies/``. - -#. Run the phoneme extraction and duration calculation script. - This script will set up a Conda environment to download and install MFA and its dependencies, so make sure that you - have Anaconda or Miniconda before running the script. - - Also note that MFA does sometimes have trouble finding OpenBlas, so you may have to manually install it with the - command ``sudo apt-get install libopenblas-dev``. - - .. code-block:: bash - - ./extract_ljspeech_phonemes_and_durs.sh - - This script takes additional options ``--skip_env_setup`` if you have already set up the ``aligner`` environment, - and ``--g2p_dict=`` if you have already downloaded CMU dict or another G2P dictionary you prefer. - - The alignment step will take a while to run, so be prepared to wait upwards of an hour. - - In addition to alignments and durations, this script will create the mappings and ignore files as well. - It will also generate some intermediate files that you are free to delete, as you only need the files listed above. - -When the following steps are finished, your ```` directory will look like this. -(Starred files and directories indicate files from intermediate steps that are safe to remove, but that you may want -to keep for bookkeeping purposes.) - -.. code:: - - / - ├── alignments/ * - ├── cmudict.dict * - ├── energies/ - │   ├── LJ001-0001.npy - │   └── ... - ├── ljs_audio_text_test_filelist.txt * - ├── ljs_audio_text_train_filelist.txt * - ├── ljs_audio_text_val_filelist.txt * - ├── ljspeech_test.json - ├── ljspeech_train.json - ├── ljspeech_val.json - ├── mappings.json - ├── metadata.csv - ├── phoneme_durations/ - │   ├── LJ001-0001.pt - │   └── ... - ├── pitches/ - │   ├── LJ001-0001.npy - │   └── ... - ├── README - ├── uncommented_cmudict.dict * - ├── wavs/ - │   ├── LJ001-0001.wav - │   └── ... - └── wavs_to_ignore.pkl diff --git a/examples/asr/conf/vad/vad_inference_postprocessing.yaml b/examples/asr/conf/vad/vad_inference_postprocessing.yaml index f51d5480bd2b..88ea3c8567f2 100644 --- a/examples/asr/conf/vad/vad_inference_postprocessing.yaml +++ b/examples/asr/conf/vad/vad_inference_postprocessing.yaml @@ -14,20 +14,20 @@ prepare_manifest: vad: - model_path: "vad_marblenet" #.nemo local model path or pretrained model name or none - parameters: # Tuned parameter for CH109! (with 11 moved multi-speech sessions as dev set) + model_path: "vad_multilingual_marblenet" #.nemo local model path or pretrained model name or none + parameters: # Parameters were tuned on 0~20db SNR noisy and clean multilingual ASR data. normalize_audio: False - window_length_in_sec: 0.15 # window length in sec for VAD context input - shift_length_in_sec: 0.01 # shift length in sec for generate frame level VAD prediction - smoothing: "median" # false or type of smoothing method (eg: median) - overlap: 0.875 # overlap ratio for overlapped mean/median smoothing filter - postprocessing: - onset: 0.4 # onset threshold for detecting the beginning and end of a speech - offset: 0.7 # offset threshold for detecting the end of a speech. - pad_onset: 0.05 # adding durations before each speech segment - pad_offset: -0.1 # adding durations after each speech segment - min_duration_on: 0.2 # threshold for small non_speech deletion - min_duration_off: 0.2 # threshold for short speech segment deletion + window_length_in_sec: 0.63 # window length in sec for VAD context input + shift_length_in_sec: 0.08 # shift length in sec for generate frame level VAD prediction, Here we use 0.08 for faster inferene + smoothing: False # false or type of smoothing method (eg: median, mean) + overlap: 0.875 # overlap ratio for overlapped mean/median smoothing filter. If smoothing=False, ignore this value. + postprocessing: + onset: 0.5 # onset threshold for detecting the beginning and end of a speech + offset: 0.3 # offset threshold for detecting the end of a speech. + pad_onset: 0.2 # adding durations before each speech segment + pad_offset: 0.2 # adding durations after each speech segment + min_duration_on: 0.5 # threshold for small non_speech deletion + min_duration_off: 0.5 # threshold for short speech segment deletion filter_speech_first: True prepared_manifest_vad_input: null # if not specify, it will automatically generated be "manifest_vad_input.json" diff --git a/examples/nlp/language_modeling/megatron_gpt_prompt_learning_eval.py b/examples/nlp/language_modeling/megatron_gpt_prompt_learning_eval.py index 6e7d2d35fbe5..826ed730536e 100644 --- a/examples/nlp/language_modeling/megatron_gpt_prompt_learning_eval.py +++ b/examples/nlp/language_modeling/megatron_gpt_prompt_learning_eval.py @@ -134,7 +134,7 @@ def main(cfg) -> None: max_input_length = model.frozen_model.cfg.encoder_seq_length - length_params["max_length"] _, dataloader = model.build_virtual_prompt_dataset( - dataset_paths=cfg.data_paths, + data=cfg.data_paths, batch_size=64, max_seq_length=max_input_length, min_seq_length=model.cfg.data.get('min_seq_length', 1), diff --git a/examples/speaker_tasks/diarization/README.md b/examples/speaker_tasks/diarization/README.md index f4925764b4e2..501fa6fc73a0 100644 --- a/examples/speaker_tasks/diarization/README.md +++ b/examples/speaker_tasks/diarization/README.md @@ -17,6 +17,7 @@ Documentation section for speaker related tasks can be found at: - [speakerverification_speakernet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/speakerverification_speakernet) ## Supported Pretrained VAD models +- [vad_multilingual_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet) - [vad_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_marblenet) - [vad_telephony_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_telephony_marblenet) @@ -90,14 +91,14 @@ You could also download *.nemo files from [this link](https://ngc.nvidia.com/cat - **`diarizer.vad.model_path`: voice activity detection modle name or path to the model** -Specify the name of VAD model, then the script will download the model from NGC. Currently, we have 'vad_marblenet' and 'vad_telephony_marblenet' as options for VAD models. +Specify the name of VAD model, then the script will download the model from NGC. Currently, we have 'vad_multilingual_marblenet', 'vad_marblenet' and 'vad_telephony_marblenet' as options for VAD models. -`diarizer.vad.model_path='vad_telephony_marblenet'` +`diarizer.vad.model_path='vad_multilingual_marblenet'` -Instead, you can also download the model from [vad_marblenet](https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet) and [vad_telephony_marblenet](https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet) and specify the full path name to the model as below. +Instead, you can also download the model from [vad_multilingual_marblenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet), [vad_marblenet](https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet) and [vad_telephony_marblenet](https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet) and specify the full path name to the model as below. -`diarizer.vad.model_path='path/to/vad_telephony_marblenet.nemo'` +`diarizer.vad.model_path='path/to/vad_multilingual_marblenet.nemo'` - **`diarizer.speaker_embeddings.parameters.multiscale_weights`: multiscale diarization (Experimental)** diff --git a/examples/speaker_tasks/diarization/conf/offline_diarization.yaml b/examples/speaker_tasks/diarization/conf/offline_diarization.yaml index ac95c6de93c8..47f0f55a73f9 100644 --- a/examples/speaker_tasks/diarization/conf/offline_diarization.yaml +++ b/examples/speaker_tasks/diarization/conf/offline_diarization.yaml @@ -19,12 +19,12 @@ diarizer: window_length_in_sec: 0.15 # Window length in sec for VAD context input shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction smoothing: "median" # False or type of smoothing method (eg: median) - overlap: 0.875 # Overlap ratio for overlapped mean/median smoothing filter - onset: 0.4 # Onset threshold for detecting the beginning and end of a speech - offset: 0.7 # Offset threshold for detecting the end of a speech - pad_onset: 0.05 # Adding durations before each speech segment - pad_offset: -0.1 # Adding durations after each speech segment - min_duration_on: 0.2 # Threshold for small non_speech deletion + overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter + onset: 0.1 # Onset threshold for detecting the beginning and end of a speech + offset: 0.1 # Offset threshold for detecting the end of a speech + pad_onset: 0.1 # Adding durations before each speech segment + pad_offset: 0 # Adding durations after each speech segment + min_duration_on: 0 # Threshold for small non_speech deletion min_duration_off: 0.2 # Threshold for short speech segment deletion filter_speech_first: True diff --git a/examples/speaker_tasks/diarization/conf/offline_diarization_with_asr.yaml b/examples/speaker_tasks/diarization/conf/offline_diarization_with_asr.yaml index 475f950071a7..4436bfda58d0 100644 --- a/examples/speaker_tasks/diarization/conf/offline_diarization_with_asr.yaml +++ b/examples/speaker_tasks/diarization/conf/offline_diarization_with_asr.yaml @@ -19,12 +19,12 @@ diarizer: window_length_in_sec: 0.15 # Window length in sec for VAD context input shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction smoothing: "median" # False or type of smoothing method (eg: median) - overlap: 0.875 # Overlap ratio for overlapped mean/median smoothing filter - onset: 0.4 # Onset threshold for detecting the beginning and end of a speech - offset: 0.7 # Offset threshold for detecting the end of a speech - pad_onset: 0.05 # Adding durations before each speech segment - pad_offset: -0.1 # Adding durations after each speech segment - min_duration_on: 0.2 # Threshold for small non_speech deletion + overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter + onset: 0.1 # Onset threshold for detecting the beginning and end of a speech + offset: 0.1 # Offset threshold for detecting the end of a speech + pad_onset: 0.1 # Adding durations before each speech segment + pad_offset: 0 # Adding durations after each speech segment + min_duration_on: 0 # Threshold for small non_speech deletion min_duration_off: 0.2 # Threshold for short speech segment deletion filter_speech_first: True diff --git a/examples/tts/conf/aligner.yaml b/examples/tts/conf/aligner.yaml index cba18e72a6b4..ff36804a3a9a 100644 --- a/examples/tts/conf/aligner.yaml +++ b/examples/tts/conf/aligner.yaml @@ -19,7 +19,7 @@ lowfreq: 0 highfreq: 8000 window: hann -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" diff --git a/examples/tts/conf/de/fastpitch_align_44100.yaml b/examples/tts/conf/de/fastpitch_align_44100.yaml index be71a1c25e48..36ca8fb74632 100644 --- a/examples/tts/conf/de/fastpitch_align_44100.yaml +++ b/examples/tts/conf/de/fastpitch_align_44100.yaml @@ -71,7 +71,6 @@ model: punct: true apostrophe: true pad_with_space: true - phonemes: true train_ds: dataset: @@ -96,7 +95,7 @@ model: pitch_norm: true pitch_mean: ${model.pitch_mean} pitch_std: ${model.pitch_std} - use_beta_binomial_interpolator: true + use_beta_binomial_interpolator: false dataloader_params: drop_last: false shuffle: true @@ -127,7 +126,7 @@ model: pitch_norm: true pitch_mean: ${model.pitch_mean} pitch_std: ${model.pitch_std} - use_beta_binomial_interpolator: true + use_beta_binomial_interpolator: false dataloader_params: drop_last: false diff --git a/examples/tts/conf/fastpitch_align_44100.yaml b/examples/tts/conf/fastpitch_align_44100.yaml index 3da851f17465..95442a0eb44f 100644 --- a/examples/tts/conf/fastpitch_align_44100.yaml +++ b/examples/tts/conf/fastpitch_align_44100.yaml @@ -27,8 +27,8 @@ lowfreq: 0 highfreq: null window: hann -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" -heteronyms_path: "scripts/tts_dataset_files/heteronyms-030921" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" model: diff --git a/examples/tts/conf/fastpitch_align_ipa.yaml b/examples/tts/conf/fastpitch_align_ipa.yaml index a44c58b42a27..2e6819293110 100644 --- a/examples/tts/conf/fastpitch_align_ipa.yaml +++ b/examples/tts/conf/fastpitch_align_ipa.yaml @@ -27,7 +27,7 @@ lowfreq: 0 highfreq: 8000 window: hann -phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.06.txt" +phoneme_dict_path: "scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.08.txt" heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" diff --git a/examples/tts/conf/fastpitch_align_v1.05.yaml b/examples/tts/conf/fastpitch_align_v1.05.yaml index c74d2b42517b..692b2500cb29 100644 --- a/examples/tts/conf/fastpitch_align_v1.05.yaml +++ b/examples/tts/conf/fastpitch_align_v1.05.yaml @@ -27,8 +27,8 @@ lowfreq: 0 highfreq: 8000 window: hann -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" -heteronyms_path: "scripts/tts_dataset_files/heteronyms-030921" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" model: diff --git a/examples/tts/conf/mixer-tts.yaml b/examples/tts/conf/mixer-tts.yaml index c9fb086e2496..c66aac76d446 100644 --- a/examples/tts/conf/mixer-tts.yaml +++ b/examples/tts/conf/mixer-tts.yaml @@ -27,8 +27,8 @@ lowfreq: 0 highfreq: 8000 window: hann -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" -heteronyms_path: "scripts/tts_dataset_files/heteronyms-030921" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" model: diff --git a/examples/tts/conf/rad-tts_dec.yaml b/examples/tts/conf/rad-tts_dec.yaml index da12a8dacabc..5e65ace49918 100644 --- a/examples/tts/conf/rad-tts_dec.yaml +++ b/examples/tts/conf/rad-tts_dec.yaml @@ -28,7 +28,7 @@ highfreq: 8000 window: "hann" -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" model: diff --git a/examples/tts/conf/rad-tts_feature_pred.yaml b/examples/tts/conf/rad-tts_feature_pred.yaml index 5202d796b5bf..e4aaed8f6d0e 100644 --- a/examples/tts/conf/rad-tts_feature_pred.yaml +++ b/examples/tts/conf/rad-tts_feature_pred.yaml @@ -27,7 +27,7 @@ lowfreq: 0 highfreq: 8000 window: "hann" -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" model: diff --git a/examples/tts/conf/tacotron2.yaml b/examples/tts/conf/tacotron2.yaml index e9e0aa4ece0b..e227a82d49af 100644 --- a/examples/tts/conf/tacotron2.yaml +++ b/examples/tts/conf/tacotron2.yaml @@ -9,8 +9,8 @@ validation_datasets: ??? sup_data_path: null sup_data_types: null -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" -heteronyms_path: "scripts/tts_dataset_files/heteronyms-030921" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" diff --git a/nemo/collections/asr/models/classification_models.py b/nemo/collections/asr/models/classification_models.py index 5eeb4f391a64..1a0eef111ab9 100644 --- a/nemo/collections/asr/models/classification_models.py +++ b/nemo/collections/asr/models/classification_models.py @@ -414,6 +414,13 @@ def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: """ results = [] + model = PretrainedModelInfo( + pretrained_model_name="vad_multilingual_marblenet", + description="For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/vad_multilingual_marblenet", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_multilingual_marblenet/versions/1.10.0/files/vad_multilingual_marblenet.nemo", + ) + results.append(model) + model = PretrainedModelInfo( pretrained_model_name="vad_telephony_marblenet", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet", diff --git a/nemo/collections/asr/parts/utils/transcribe_utils.py b/nemo/collections/asr/parts/utils/transcribe_utils.py index 999e53aea41e..244231ca92f8 100644 --- a/nemo/collections/asr/parts/utils/transcribe_utils.py +++ b/nemo/collections/asr/parts/utils/transcribe_utils.py @@ -75,7 +75,9 @@ def transcribe_partial_audio( hypotheses.append(lg.cpu().numpy()) else: current_hypotheses, _ = asr_model._wer.decoding.ctc_decoder_predictions_tensor( - greedy_predictions, predictions_len=logits_len, return_hypotheses=return_hypotheses, + decoder_outputs=greedy_predictions, + decoder_lengths=logits_len, + return_hypotheses=return_hypotheses, ) if return_hypotheses: diff --git a/nemo/collections/nlp/data/language_modeling/megatron/gpt_prompt_learning_dataset.py b/nemo/collections/nlp/data/language_modeling/megatron/gpt_prompt_learning_dataset.py index 44f34299fee3..9f91a9b87cfb 100755 --- a/nemo/collections/nlp/data/language_modeling/megatron/gpt_prompt_learning_dataset.py +++ b/nemo/collections/nlp/data/language_modeling/megatron/gpt_prompt_learning_dataset.py @@ -30,7 +30,7 @@ class GPTPromptLearningDataset(Dataset): The dataset class for prompt-tuning or p-tuning pretrained GPT models. Args: - dataset_paths (list[strings]): paths to .jsonl or .json files + data (list[strings], list[dicts]): (1) paths to .jsonl or .json files, (2) dict objects corresponding to each input example tokenizer (tokenizer): Tokenizer from frozen language model virtual_prompt_source (Enum): Either VirtualPromptSource.PROMPT_TABLE or VirtualPromptSource.PROMPT_ENCODER task_templates (dict): Dictionary containing all task template information needed to format prompts. Created in the GPTPromptLearningModel class. @@ -46,7 +46,7 @@ class GPTPromptLearningDataset(Dataset): def __init__( self, - dataset_paths, + data, tokenizer, virtual_prompt_source: VirtualPromptSource, task_templates: dict, @@ -80,13 +80,17 @@ def __init__( logging.info("Loading and tokenizing dataset ... ") + # Data is just a list of dicts already loaded from a json file or passed in directly as a dict + if isinstance(data[0], dict): + self.load_data(data) + # Datasets are a list of file path strings to .json or .jsonl files - if isinstance(dataset_paths[0], str): - for path in dataset_paths: + elif isinstance(data[0], str): + for path in data: dataset = open(path, 'r', encoding='utf-8') self.load_data(dataset) else: - raise ValueError("Datasets must be a list of filepath strings") + raise ValueError("Datasets must be a list of filepath strings or a list of data example dicts") def load_data(self, dataset): """ diff --git a/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py b/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py index 6256566819e6..861676acf316 100644 --- a/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py +++ b/nemo/collections/nlp/models/duplex_text_normalization/duplex_decoder.py @@ -567,4 +567,11 @@ def list_available_models(cls) -> Optional[PretrainedModelInfo]: description="Text Normalization model's decoder model.", ) ) + result.append( + PretrainedModelInfo( + pretrained_model_name="itn_en_t5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/itn_en_t5/versions/1.11.0/files/itn_en_t5_decoder.nemo", + description="English Inverse Text Normalization model's decoder model.", + ) + ) return result diff --git a/nemo/collections/nlp/models/duplex_text_normalization/duplex_tagger.py b/nemo/collections/nlp/models/duplex_text_normalization/duplex_tagger.py index eb8f1cdb50bb..64a2e8df1f62 100644 --- a/nemo/collections/nlp/models/duplex_text_normalization/duplex_tagger.py +++ b/nemo/collections/nlp/models/duplex_text_normalization/duplex_tagger.py @@ -387,4 +387,11 @@ def list_available_models(cls) -> Optional[PretrainedModelInfo]: description="Text Normalization model's tagger model.", ) ) + result.append( + PretrainedModelInfo( + pretrained_model_name="itn_en_t5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/itn_en_t5/versions/1.11.0/files/itn_en_t5_tagger.nemo", + description="English Inverse Text Normalization model's tagger model.", + ) + ) return result diff --git a/nemo/collections/nlp/models/language_modeling/megatron_gpt_prompt_learning_model.py b/nemo/collections/nlp/models/language_modeling/megatron_gpt_prompt_learning_model.py index e092eb70ecbb..6db3d8fa310c 100644 --- a/nemo/collections/nlp/models/language_modeling/megatron_gpt_prompt_learning_model.py +++ b/nemo/collections/nlp/models/language_modeling/megatron_gpt_prompt_learning_model.py @@ -686,7 +686,7 @@ def setup(self, stage=None): def setup_training_data(self, training_data_config=None): if self.cfg.data.get('train_ds', None): self._train_ds, self._train_dl = self.build_virtual_prompt_dataset( - dataset_paths=self.cfg.data.train_ds, + data=self.cfg.data.train_ds, batch_size=self.cfg.global_batch_size, max_seq_length=self.frozen_model.cfg.encoder_seq_length, min_seq_length=self.cfg.data.get('min_seq_length', 1), @@ -702,7 +702,7 @@ def setup_training_data(self, training_data_config=None): def setup_validation_data(self, validation_data_config=None): if self.cfg.data.get('validation_ds', None): self._validation_ds, self._validation_dl = self.build_virtual_prompt_dataset( - dataset_paths=self.cfg.data.validation_ds, + data=self.cfg.data.validation_ds, batch_size=self.cfg.global_batch_size, max_seq_length=self.frozen_model.cfg.encoder_seq_length, min_seq_length=self.cfg.data.get('min_seq_length', 1), @@ -718,7 +718,7 @@ def setup_validation_data(self, validation_data_config=None): def setup_test_data(self, test_data_config=None): if self.cfg.data.get('test_ds', None): self._test_ds, self._test_dl = self.build_virtual_prompt_dataset( - dataset_paths=self.cfg.data.test_ds, + data=self.cfg.data.test_ds, batch_size=self.cfg.global_batch_size, max_seq_length=self.frozen_model.cfg.encoder_seq_length, min_seq_length=self.cfg.data.get('min_seq_length', 1), @@ -733,7 +733,7 @@ def setup_test_data(self, test_data_config=None): def build_virtual_prompt_dataset( self, - dataset_paths, + data, batch_size=None, max_seq_length=2048, min_seq_length=1, @@ -748,7 +748,7 @@ def build_virtual_prompt_dataset( get_dataset_only=False, ): dataset = GPTPromptLearningDataset( - dataset_paths=dataset_paths, + data=data, tokenizer=self.tokenizer, virtual_prompt_source=self.virtual_prompt_source, task_templates=self.task_templates, @@ -885,9 +885,14 @@ def dummy(): max_input_length = self.frozen_model.cfg.encoder_seq_length - length_params["max_length"] - dataset_paths = [path["data_path"] for path in inputs] + # input dicts are either dataset paths or already loaded example dicts + if "taskname" not in inputs[0].keys(): + data = [path["data_path"] for path in inputs] + else: + data = inputs + dataset = self.build_virtual_prompt_dataset( - dataset_paths=dataset_paths, + data=data, max_seq_length=max_input_length, min_seq_length=self.cfg.data.get('min_seq_length', 1), add_bos=sampling_params["add_BOS"], diff --git a/nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py b/nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py index eee709d8be6b..df64ee7c4b57 100644 --- a/nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py +++ b/nemo/collections/nlp/models/language_modeling/megatron_lm_encoder_decoder_model.py @@ -427,7 +427,7 @@ def _kwargs_to_arg_idx(self): """ Returns a dict {kwarg name: arg index} to be used when mapping kwargs into a list of args. - + Computed on first call, and then cached. """ # build mapping of kwargs to arg index at first run @@ -439,7 +439,7 @@ def _kwargs_to_arg_idx(self): def _build_forward_args_from_kwargs(self, args_name, args, **kwargs): """ A helper method that converts arguments into positional arguments (by name) - + args - a list of arguments to pass to self.enc_dec_model (tensors from batch) args_name - a list of argument name (to be matched against allowed kwargs) kwargs - a dict {arg name: arg value} (used for non-tensor values) @@ -846,7 +846,7 @@ def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: Optional[int] def encode(self, tokens_enc, enc_mask, encoder_input=None, reconfigure_microbatch=True): """ - tokens_enc - encoder input tokens + tokens_enc - encoder input tokens enc_mask - corresponding mask encoder_input - encoder input (bypass tokens), if given tokens_enc can be None. """ @@ -996,9 +996,9 @@ def dummy(): device = tokens_enc.device encoder_seq_length = tokens_enc.size(1) else: - global_batch_per_gpu = enc_output.size(1) + global_batch_per_gpu = enc_output.size(0) device = enc_output.device - encoder_seq_length = enc_output.size(0) + encoder_seq_length = enc_output.size(1) num_micro_batches_before_decode = get_num_microbatches() # Reconfigure microbatch calculator here to set num microbatches to 1 while decoding since its not clear how to decode with "grad acc". @@ -1168,7 +1168,7 @@ def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_ """ PTL hook: https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_module.html#transfer-batch-to-device When using pipeline parallelism, we need the global batch to remain on the CPU, since the memory overhead will be too high when using a large number of microbatches. - Microbatches are transferred from CPU to GPU inside the pipeline. + Microbatches are transferred from CPU to GPU inside the pipeline. """ return batch diff --git a/nemo/collections/nlp/modules/common/megatron/token_level_encoder_decoder.py b/nemo/collections/nlp/modules/common/megatron/token_level_encoder_decoder.py index 796f5411f7fa..4e8796017440 100644 --- a/nemo/collections/nlp/modules/common/megatron/token_level_encoder_decoder.py +++ b/nemo/collections/nlp/modules/common/megatron/token_level_encoder_decoder.py @@ -410,7 +410,11 @@ def forward( # When pipeline parallel > 1 we need to make sure encoder exist (will be missing in decoder) if enc_output is None and self.enc_dec_model.encoder is not None: enc_output = self.enc_dec_model.encode( - enc_input=enc_input, enc_attn_mask=enc_attn_mask, enc_layer_past=None, enc_get_key_value=False, + enc_input=enc_input, + enc_attn_mask=enc_attn_mask, + enc_layer_past=None, + enc_get_key_value=False, + enc_self_attention_relative_position_bias=encoder_self_attention_relative_position_bias, ) else: enc_output = self.enc_dec_model.encoder_hidden_state diff --git a/nemo/collections/tts/models/aligner.py b/nemo/collections/tts/models/aligner.py index 3ea5a3a3d10c..f6078b6f2a4d 100644 --- a/nemo/collections/tts/models/aligner.py +++ b/nemo/collections/tts/models/aligner.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import List + import numpy as np import omegaconf import torch @@ -24,6 +26,7 @@ from nemo.collections.tts.helpers.helpers import binarize_attention, get_mask_from_lengths, plot_alignment_to_numpy from nemo.collections.tts.losses.aligner_loss import BinLoss, ForwardSumLoss from nemo.core.classes import ModelPT +from nemo.core.classes.common import PretrainedModelInfo from nemo.utils import logging, model_utils HAVE_WANDB = True @@ -230,6 +233,19 @@ def setup_test_data(self, cfg): pass @classmethod - def list_available_models(cls): - """Empty.""" - pass + def list_available_models(cls) -> List[PretrainedModelInfo]: + """ + This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. + Returns: + List of available pre-trained models. + """ + list_of_models = [] + model = PretrainedModelInfo( + pretrained_model_name="tts_en_radtts_aligner", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_radtts_aligner/versions/ARPABET_1.11.0/files/Aligner.nemo", + description="This model is trained on LJSpeech sampled at 22050Hz with and can be used to align text and audio.", + class_=cls, + ) + list_of_models.append(model) + + return list_of_models diff --git a/nemo/collections/tts/models/fastpitch.py b/nemo/collections/tts/models/fastpitch.py index 0fbdee7a0278..00541191066d 100644 --- a/nemo/collections/tts/models/fastpitch.py +++ b/nemo/collections/tts/models/fastpitch.py @@ -47,8 +47,8 @@ @dataclass class G2PConfig: _target_: str = "nemo.collections.common.tokenizers.text_to_speech.g2ps.EnglishG2p" - phoneme_dict: str = "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" - heteronyms: str = "scripts/tts_dataset_files/heteronyms-030921" + phoneme_dict: str = "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" + heteronyms: str = "scripts/tts_dataset_files/heteronyms-052722" phoneme_probability: float = 0.5 @@ -509,6 +509,8 @@ def list_available_models(cls) -> 'List[PretrainedModelInfo]': List of available pre-trained models. """ list_of_models = [] + + # en-US, single speaker, 22050Hz, LJSpeech. model = PretrainedModelInfo( pretrained_model_name="tts_en_fastpitch", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_fastpitch/versions/1.8.1/files/tts_en_fastpitch_align.nemo", @@ -517,6 +519,7 @@ def list_available_models(cls) -> 'List[PretrainedModelInfo]': ) list_of_models.append(model) + # en-US, multi-speaker, 44100Hz, HiFiTTS. model = PretrainedModelInfo( pretrained_model_name="tts_en_fastpitch_multispeaker", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_multispeaker_fastpitchhifigan/versions/1.10.0/files/tts_en_fastpitch_multispeaker.nemo", @@ -525,6 +528,24 @@ def list_available_models(cls) -> 'List[PretrainedModelInfo]': ) list_of_models.append(model) + # de-DE, single speaker, 22050 Hz, OpenSLR Neutral German Dataset. + model = PretrainedModelInfo( + pretrained_model_name="tts_de_fastpitch_singlespeaker", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_de_fastpitchhifigan/versions/1.10.0/files/tts_de_fastpitch_align.nemo", + description="This model is trained on a single male speaker data in OpenSLR Neutral German Dataset sampled at 22050Hz and can be used to generate male German voices.", + class_=cls, + ) + list_of_models.append(model) + + # de-DE, multi-speaker, 5 speakers, 44100 Hz, HUI-Audio-Corpus-German Clean. + model = PretrainedModelInfo( + pretrained_model_name="tts_de_fastpitch_multispeaker_5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_de_fastpitch_multispeaker_5/versions/1.11.0/files/tts_de_fastpitch_multispeaker_5.nemo", + description="This model is trained on 5 speakers in HUI-Audio-Corpus-German clean subset sampled at 44100Hz with and can be used to generate male and female German voices.", + class_=cls, + ) + list_of_models.append(model) + return list_of_models # Methods for model exportability @@ -538,9 +559,9 @@ def _prepare_for_export(self, **kwargs): "text": NeuralType(tensor_shape, TokenIndex()), "pitch": NeuralType(tensor_shape, RegressionValuesType()), "pace": NeuralType(tensor_shape), - "speaker": NeuralType(('B'), Index(), optional=True), "volume": NeuralType(tensor_shape, optional=True), "batch_lengths": NeuralType(('B'), optional=True), + "speaker": NeuralType(('B'), Index(), optional=True), } self._output_types = { "spect": NeuralType(('B', 'D', 'T'), MelSpectrogramType()), @@ -582,7 +603,7 @@ def input_example(self, max_batch=1, max_dim=44): A tuple of input examples. """ par = next(self.fastpitch.parameters()) - sz = (max_batch * max_dim) if self.export_config["enable_ragged_batches"] else (max_batch, max_dim) + sz = (max_batch * max_dim,) if self.export_config["enable_ragged_batches"] else (max_batch, max_dim) inp = torch.randint( 0, self.fastpitch.encoder.word_emb.num_embeddings, sz, device=par.device, dtype=torch.int64 ) @@ -596,7 +617,7 @@ def input_example(self, max_batch=1, max_dim=44): inputs['volume'] = volume if self.export_config["enable_ragged_batches"]: batch_lengths = torch.zeros((max_batch + 1), device=par.device, dtype=torch.int32) - left_over_size = sz + left_over_size = sz[0] batch_lengths[0] = 0 for i in range(1, max_batch): length = torch.randint(1, left_over_size - (max_batch - i), (1,), device=par.device) @@ -609,7 +630,7 @@ def input_example(self, max_batch=1, max_dim=44): while index < len(batch_lengths): sum += batch_lengths[index] - batch_lengths[index - 1] index += 1 - assert sum == sz, f"sum: {sum}, sz: {sz}, lengths:{batch_lengths}" + assert sum == sz[0], f"sum: {sum}, sz: {sz[0]}, lengths:{batch_lengths}" inputs['batch_lengths'] = batch_lengths if self.fastpitch.speaker_emb is not None: @@ -622,7 +643,7 @@ def input_example(self, max_batch=1, max_dim=44): def forward_for_export(self, text, pitch, pace, volume=None, batch_lengths=None, speaker=None): if self.export_config["enable_ragged_batches"]: text, pitch, pace, volume_tensor = create_batch( - text, pitch, pace, volume, batch_lengths, padding_idx=self.fastpitch.encoder.padding_idx + text, pitch, pace, batch_lengths, padding_idx=self.fastpitch.encoder.padding_idx, volume=volume ) if volume is not None: volume = volume_tensor diff --git a/nemo/collections/tts/models/hifigan.py b/nemo/collections/tts/models/hifigan.py index fb6d5e678115..ac0e8987da4b 100644 --- a/nemo/collections/tts/models/hifigan.py +++ b/nemo/collections/tts/models/hifigan.py @@ -380,6 +380,28 @@ def list_available_models(cls) -> 'Optional[Dict[str, str]]': ) list_of_models.append(model) + # de-DE, single speaker, 22050 Hz, OpenSLR Neutral German Dataset. + model = PretrainedModelInfo( + pretrained_model_name="tts_de_slr_hifigan_ft_fastpitch_singlespeaker", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_de_fastpitchhifigan/versions/1.10.0/files/tts_de_hifigan.nemo", + description="This model is finetuned from the HiFiGAN pretrained checkpoint `tts_hifigan` " + "by the mel-spectrograms generated from the FastPitch checkpoint `tts_de_fastpitch_singlespeaker`. This model " + "has been tested on generating male German voices.", + class_=cls, + ) + list_of_models.append(model) + + # de-DE, multi-speaker, 5 speakers, 44100 Hz, HUI-Audio-Corpus-German Clean. + model = PretrainedModelInfo( + pretrained_model_name="tts_de_hui_hifigan_ft_fastpitch_multispeaker_5", + location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_de_fastpitch_multispeaker_5/versions/1.11.0/files/tts_de_hui_hifigan_ft_fastpitch_multispeaker_5.nemo", + description="This model is finetuned from the HiFiGAN pretrained checkpoint `tts_en_hifitts_hifigan_ft_fastpitch` " + "by the mel-spectrograms generated from the FastPitch checkpoint `tts_de_fastpitch_multispeaker_5`. This model " + "has been tested on generating male and female German voices.", + class_=cls, + ) + list_of_models.append(model) + return list_of_models def load_state_dict(self, state_dict, strict=True): diff --git a/nemo/collections/tts/torch/tts_dataset.yaml b/nemo/collections/tts/torch/tts_dataset.yaml index f9380005f5d5..d693b62e08da 100644 --- a/nemo/collections/tts/torch/tts_dataset.yaml +++ b/nemo/collections/tts/torch/tts_dataset.yaml @@ -42,5 +42,5 @@ tts_dataset: pad_with_space: True g2p: _target_: nemo.collections.common.tokenizers.text_to_speech.g2ps.EnglishG2p - phoneme_dict: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" - heteronyms: "scripts/tts_dataset_files/heteronyms-030921" + phoneme_dict: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" + heteronyms: "scripts/tts_dataset_files/heteronyms-052722" diff --git a/nemo/package_info.py b/nemo/package_info.py index 38434a82c111..d9b7f45f8407 100644 --- a/nemo/package_info.py +++ b/nemo/package_info.py @@ -14,7 +14,7 @@ MAJOR = 1 -MINOR = 11 +MINOR = 12 PATCH = 0 PRE_RELEASE = 'rc0' diff --git a/scripts/dataset_processing/ljspeech/README b/scripts/dataset_processing/ljspeech/README deleted file mode 100644 index d092fa15958d..000000000000 --- a/scripts/dataset_processing/ljspeech/README +++ /dev/null @@ -1,16 +0,0 @@ -NOTE: this folder will be removed in 1.8.0 version. Please, use scripts/dataset_processing/tts/ljspeech instead. - -These scripts are used for extracting features from LJSpeech. - -You will need to run the following scripts (note that these will call the other -scripts as necessary): - -- `create_manifests_and_textfiles.py`: Creates the manifest and text files that - are used by the Montreal Forced Aligner (MFA) library. -- `extract_ljspeech_phonemes_and_durs.sh`: Extracts phonemes and alignments via - MFA and calculates phoneme durations. - Needs to be run with Conda active since it creates a Conda environment. - Also note that you may need to install OpenBlas manually (e.g. sudo apt-get - install libopenblas-dev), sometimes MFA can't find it if installed via Conda. - (This script calls `create_token2idx_dict.py` and `calculate_durs.py`.) -- `extract_ljspeech_energy_pitch.py` diff --git a/scripts/dataset_processing/ljspeech/calculate_durs.py b/scripts/dataset_processing/ljspeech/calculate_durs.py deleted file mode 100644 index f7a37c2a2ee8..000000000000 --- a/scripts/dataset_processing/ljspeech/calculate_durs.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Calculates durations for LJSpeech based on MFA TextGrid alignments. -""" -import argparse -import glob -import json -import os -import pickle -from math import ceil - -import numpy as np -import tgt -import torch -from tqdm import tqdm - -parser = argparse.ArgumentParser(description="Calculates phoneme durations for LJSpeech from TextGrids.") -parser.add_argument('--ljspeech_dir', required=True, default=None, type=str) -parser.add_argument( - '--mappings', - required=False, - default=None, - type=str, - help='JSON file of mappings created with create_token2idx_dict.py', -) -parser.add_argument('--sr', required=False, default=22050, type=int) -parser.add_argument('--hop_length', required=False, default=256, type=int) -args = parser.parse_args() - - -def calculate_durations(textgrid, phone2idx): - tokens = [] - durs = [] - - frames_per_second = args.sr / args.hop_length - tg = tgt.read_textgrid(textgrid, include_empty_intervals=True) - data_tier = tg.get_tier_by_name("phones") - - # Get total frames - total_frames = ceil((data_tier.end_time - data_tier.start_time) * frames_per_second) - - # Find start and end frames of each token - se_in_frames = np.array([(frames_per_second * d.start_time, frames_per_second * d.end_time) for d in data_tier]) - se_in_frames = np.round(se_in_frames) - durs = (se_in_frames[:, 1] - se_in_frames[:, 0]).astype(int) - blank_set = ('sil', 'sp', 'spn', '', '') - blank_token = " " - - # merge repeated blank tokens - tokens, durations = [], [] - for i in range(len(data_tier)): - x = data_tier[i].text - if x == 'spn': - return None, None, None - x = blank_token if x in blank_set else x - - if len(tokens) and tokens[-1] == blank_token and x == blank_token: - durations[-1] += durs[i] - else: - tokens.append(x) - durations.append(durs[i]) - - tokens_enc = [phone2idx[token] for token in tokens] - tokens_enc, durations = torch.LongTensor(tokens_enc), torch.LongTensor(durations) - - # Add rounding error to final token - durations[-1] += total_frames - durations.sum() - - return tokens, tokens_enc, durations - - -def main(): - textgrid_list = glob.glob(os.path.join(args.ljspeech_dir, 'alignments/wavs/*.TextGrid')) - - # Create target_dir if necessary - target_dir = os.path.join(args.ljspeech_dir, 'phoneme_durations/') - print(f"Calculating phoneme durations, files will be in: {target_dir}") - - if not os.path.exists(target_dir): - print(f"Creating {target_dir}") - os.mkdir(target_dir) - - # Read phoneme to idx mappings - phone2idx = None - if args.mappings: - with open(args.mappings, 'r') as f: - mappings = json.load(f) - phone2idx = mappings['phone2idx'] - - oov_samples = [] - - # Iterate through all TextGrid files - for textgrid in tqdm(textgrid_list): - basename = os.path.splitext(os.path.basename(textgrid))[0][5:] # Chop off 'wavs_' prefix - - phones_mfa, tokens_mfa, durs = calculate_durations(textgrid, phone2idx) - - if phones_mfa is None: - oov_samples.append(basename) - continue - - # Save to file - target_path = os.path.join(target_dir, f'{basename}.pt') - torch.save({'text_encoded': tokens_mfa, 'token_duration': durs}, target_path) - - print(f"Getting rid of {len(oov_samples)} samples with OOV words.") - oov_target = os.path.join(args.ljspeech_dir, 'wavs_to_ignore.pkl') - with open(oov_target, 'wb') as f: - pickle.dump(oov_samples, f) - print(f"List of OOV samples written to: {oov_target}") - - -if __name__ == '__main__': - main() diff --git a/scripts/dataset_processing/ljspeech/create_manifests_and_textfiles.py b/scripts/dataset_processing/ljspeech/create_manifests_and_textfiles.py deleted file mode 100644 index a6cb78804e7e..000000000000 --- a/scripts/dataset_processing/ljspeech/create_manifests_and_textfiles.py +++ /dev/null @@ -1,130 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -This script creates NeMo manifests which can be used for training models on LJSpeech (split is taken from https://github.com/NVIDIA/tacotron2). -LJSpeech can be downloaded via --download_ljspeech flag. -It optionally saves transcripts in .txt files (can be used for extracting durations via MFA library). -""" - -import argparse -import json -import os - -import sox -import wget -from nemo_text_processing.text_normalization.normalize import Normalizer -from scripts.dataset_processing.ljspeech.get_lj_speech_data import main as get_lj_speech - -from nemo.collections.common.parts.preprocessing import parsers - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - '--ljspeech_dir', - required=True, - type=str, - help="Path to folder with LJSpeech dataset or folder where to download it (in this case, additionally specify --download_ljspeech).", - ) - parser.add_argument('--download_ljspeech', action='store_true', default=False) - parser.add_argument('--normalizer_class', choices=["ENCharParser", "Normalizer"], default="Normalizer", type=str) - parser.add_argument('--whitelist_path', type=str, default=None) - parser.add_argument('--save_transcripts_in_txt', action='store_true', default=False) - parser.add_argument( - '--manifest_text_var_is_normalized', - action='store_true', - default=False, - help="If specified, the text in the manifest will contain normalized text. Otherwise, the text will contain the original text.", - ) - - args = parser.parse_args() - return args - - -def main(): - args = get_args() - ljspeech_dir = args.ljspeech_dir - - # Download LJSpeech dataset if needed - if args.download_ljspeech: - get_lj_speech(args.ljspeech_dir) - ljspeech_dir = os.path.join(args.ljspeech_dir, "LJSpeech-1.1") - - # Create normalizer - if args.normalizer_class == "ENCharParser": - normalizer_call = parsers.make_parser(name='en')._normalize - elif args.normalizer_class == "Normalizer": - whitelist_path = args.whitelist_path - - if whitelist_path is None: - wget.download( - "https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv", - out=ljspeech_dir, - ) - whitelist_path = os.path.join(ljspeech_dir, "lj_speech.tsv") - - text_normalizer = Normalizer( - lang="en", - input_case="cased", - whitelist=whitelist_path, - overwrite_cache=True, - cache_dir=os.path.join(ljspeech_dir, "cache_dir"), - ) - text_normalizer_call_kwargs = {"punct_pre_process": True, "punct_post_process": True} - - normalizer_call = lambda x: text_normalizer.normalize(x, **text_normalizer_call_kwargs) - else: - raise ValueError("normalizer_class must be ENCharParser or Normalizer") - - # Create manifests (based on predefined NVIDIA's split) and optionally save transcripts in .txt files - filelist_base = 'https://raw.githubusercontent.com/NVIDIA/tacotron2/master/filelists' - filelists = ['train', 'val', 'test'] - for split in filelists: - # Download file list if necessary - filelist_path = os.path.join(ljspeech_dir, f"ljs_audio_text_{split}_filelist.txt") - if not os.path.exists(filelist_path): - wget.download(f"{filelist_base}/ljs_audio_text_{split}_filelist.txt", out=ljspeech_dir) - - manifest_target = os.path.join(ljspeech_dir, f"ljspeech_{split}.json") - with open(manifest_target, 'w') as f_out: - with open(filelist_path, 'r') as filelist: - print(f"\nCreating {manifest_target}...") - for line in filelist: - basename = line[6:16] - - text = line[21:].strip() - norm_text = normalizer_call(text) - - # Make sure corresponding wavfile exists - wav_path = os.path.join(ljspeech_dir, 'wavs', basename + '.wav') - assert os.path.exists(wav_path) - - if args.save_transcripts_in_txt: - txt_path = os.path.join(ljspeech_dir, 'wavs', basename + '.txt') - with open(txt_path, 'w') as f_txt: - f_txt.write(norm_text) - - # Write manifest entry - entry = { - 'audio_filepath': wav_path, - 'duration': sox.file_info.duration(wav_path), - 'text': norm_text if args.manifest_text_var_is_normalized else text, - 'normalized_text': norm_text, - } - - f_out.write(json.dumps(entry) + '\n') - - -if __name__ == '__main__': - main() diff --git a/scripts/dataset_processing/ljspeech/create_token2idx_dict.py b/scripts/dataset_processing/ljspeech/create_token2idx_dict.py deleted file mode 100644 index 2d3e73a38a0d..000000000000 --- a/scripts/dataset_processing/ljspeech/create_token2idx_dict.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Creates a dictionary from token to index based on dictionary .txt given. -""" -import argparse -import json -import os - -parser = argparse.ArgumentParser() -parser.add_argument('--dictionary', required=True, default=None, type=str) -parser.add_argument('--dict_out', required=True, default=None, type=str) -args = parser.parse_args() - - -def main(): - if not os.path.exists(args.dictionary): - raise FileNotFoundError(f"Could not find dictionary file {args.dictionary}") - - phonemes = set() - word2phones = {} - with open(args.dictionary, 'r') as f: - for line in f: - line = line.split() - word = line[0] - tokens = line[1:] - - word2phones[word] = tokens - phonemes.update(tokens) - - # Small list of additional punctuation - word2phones[','] = [' '] - word2phones[';'] = [' '] - word2phones['.'] = [' '] - word2phones['!'] = [' '] - word2phones['?'] = [' '] - word2phones['"'] = [' '] - word2phones['-'] = [' '] - - phone2idx = {k: i for i, k in enumerate(phonemes)} - phone2idx[' '] = len(phone2idx) - phone2idx['sil'] = phone2idx[' '] # Silence - phone2idx['sp'] = phone2idx[' '] # Space - phone2idx['spn'] = phone2idx[' '] # OOV/unk - - dicts = { - 'phone2idx': phone2idx, - 'word2phones': word2phones, - } - with open(args.dict_out, 'w') as f: - json.dump(dicts, f) - - print(f"Total number of phone indices: {len(phone2idx)}") - - -if __name__ == '__main__': - main() diff --git a/scripts/dataset_processing/ljspeech/extract_ljspeech_energy_pitch.py b/scripts/dataset_processing/ljspeech/extract_ljspeech_energy_pitch.py deleted file mode 100644 index a76ce6dd9fdc..000000000000 --- a/scripts/dataset_processing/ljspeech/extract_ljspeech_energy_pitch.py +++ /dev/null @@ -1,88 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Extracts energy and pitch data from LJSpeech wav files and writes them to a directory as LJxxx-xxxx.npy files. -Assuming that the wavs are located at `/wavs`, this script will write the -energy files to `/energies/` and the pitch files to `/pitches`, -creating the relevant directories if necessary. - -USAGE: python extract_ljspeech_energy_pitch.py --ljspeech_dir= -""" -import argparse -from pathlib import Path - -import librosa -import numpy as np - -tqdm = None - -try: - from tqdm import tqdm -except ModuleNotFoundError: - pass - -parser = argparse.ArgumentParser( - description="Extracts energies (L2-norm of STFT frame amplitudes) and pitches (F0) from LJSpeech data." -) -parser.add_argument("--ljspeech_dir", required=True, default=None, type=str) -args = parser.parse_args() - - -def main(): - wavfile_dir = Path(args.ljspeech_dir) / "wavs" - wavfile_list = list(wavfile_dir.glob('*.wav')) - - target_dir = Path(args.ljspeech_dir) - # Create target dir /energies and /pitches if necessary - if not Path(target_dir / "energies").exists(): - print(f"Creating target directory: {target_dir/'energies'}") - Path(target_dir / "energies").mkdir() - if not Path(target_dir / "pitches").exists(): - print(f"Creating target directory: {target_dir/'pitches'}") - Path(target_dir / "pitches").mkdir() - - if tqdm is not None: - wavfile_list = tqdm(wavfile_list) - for count, file_ in enumerate(wavfile_list): - basename = Path(file_).stem - pitch_path = target_dir / "pitches" / f"{basename}.npy" - energy_path = target_dir / "energies" / f"{basename}.npy" - if pitch_path.exists() and energy_path.exists(): - continue - audio, sr = librosa.load(file_, sr=22050) - - # Calculate f0 - # Please note that fmin and fmax are good approximates for the speaker in LJSpeech and may not generalize to - # other speakers - f0, _, _ = librosa.pyin(audio, fmin=80, fmax=800, frame_length=1024, sr=sr, fill_na=0.0) - - # Save to new file - np.save(pitch_path, f0) - - # Calculate energy - stft_amplitude = np.abs(librosa.stft(audio, n_fft=1024, hop_length=256, win_length=1024)) - energy = np.linalg.norm(stft_amplitude, axis=0) # axis=0 since librosa.stft -> (freq bins, frames) - - # Save to new file - np.save(energy_path, energy) - - assert energy.shape == f0.shape - if tqdm is None and count % 1000 == 0: - print(f"Finished processing {count} wav files...") - - print(f"Finished energy extraction for a total of {len(wavfile_list)} wav files.") - - -if __name__ == '__main__': - main() diff --git a/scripts/dataset_processing/ljspeech/extract_ljspeech_phonemes_and_durs.sh b/scripts/dataset_processing/ljspeech/extract_ljspeech_phonemes_and_durs.sh deleted file mode 100755 index 04f306cb8fa4..000000000000 --- a/scripts/dataset_processing/ljspeech/extract_ljspeech_phonemes_and_durs.sh +++ /dev/null @@ -1,150 +0,0 @@ -#!/bin/bash - -# Extracts the phonemes and alignments for the LJSpeech dataset via the -# Montreal Forced Aligner (MFA) library, and computes durations per phoneme. -# Assumes you have downloaded and expanded the LJSpeech dataset, and that they -# are located at the directory specified. -# -# This script will create: -# - /mappings.json: Contains word->phone and phone->idx mappings -# - /wavs_to_ignore.pkl: A pickled list of wavs to ignore -# because of OOV words in their transcripts -# - /phoneme_durations/LJ*.npz: Numpy files for each wavfile -# with fields 'tokens' (token indices) and 'durs' (durations per token) -# -# It will also create intermediate files: -# - /alignments/wavs/LJ*.TextGrid: MFA output -# -# Note: This script will create a conda environment to set up MFA, so please -# make sure that conda is active before running this script. -# -# Note: You may need to install OpenBlas yourself if the MFA script can't find -# it. (e.g. sudo apt-get install libopenblas-dev) -# -# Example Usage: -# ./extract_ljspeech_phonemes_and_durs.sh /data/LJSpeech-1.1 - -ENV_NAME='aligner' - -SAMPLE_RATE=22050 -WINDOW_STRIDE=256 - -CMUDICT_URL='https://raw.githubusercontent.com/cmusphinx/cmudict/master/cmudict.dict' -SPLITS_BASE_URL='https://raw.githubusercontent.com/NVIDIA/tacotron2/master/filelists/' - -# Usage info -show_help() { -cat << EOF -Usage: $(basename "$0") [-h] \ - [--skip_env_setup] \ - [--g2p_dict=] \ - -Extracts phonemes and their respective durations for the LJSpeech dataset using -the Montreal Forced Aligner (MFA). - - -h Help message - --skip_env_setup (Optional) Skips setting up the MFA conda environment - "aligner". Use only if you already have this set up. - --g2p_dict (Optional) Path to the grapheme to phoneme dictionary - text file, if already generated. If set, skips the G2P - step. -EOF -} - -SKIP_ENV_SETUP=false -G2P_DICT='' - -while :; do - case $1 in - -h|-\?|--help) - show_help - exit - ;; - --g2p_dict=?*) - G2P_DICT=${1#*=} - ;; - --skip_env_setup) - SKIP_ENV_SETUP=true - ;; - *) - break - esac - shift -done - -if [[ -z $1 ]]; then - echo "Must specify a LJSpeech base directory." - exit 1 -fi -LJSPEECH_BASE=$1 - -# Check for conda -read -r -d '' CONDA_MESSAGE << EOM -This script requires either Anaconda or Miniconda to be active, as it installs the Montreal Forced Aligner via a conda environment. -See their documentation (https://montreal-forced-aligner.readthedocs.io/en/latest/installation.html) for more details. -EOM -if [ -z "$(which conda)" ]; then - echo $CONDA_MESSAGE - exit -fi - -CONDA_PREFIX=$(conda info --base) -source $CONDA_PREFIX/etc/profile.d/conda.sh - -# Set up env for MFA (env name "aligner") and install -if $SKIP_ENV_SETUP; then - echo "Skipping environment setup. Assuming env name "aligner" exists." -else - echo "Setting up conda environment for MFA (env name \"aligner\")..." - conda create -n $ENV_NAME -c conda-forge openblas python=3.8 openfst pynini ngram baumwelch - conda activate $ENV_NAME - pip install tgt torch - conda install -c conda-forge montreal-forced-aligner - echo "Conda environment \"$ENV_NAME\" set up." -fi - -if ! conda activate $ENV_NAME; then - echo "Could not activate environment, see Conda output above." - exit -fi - -# Download CMU word-to-phoneme dict and clean out comments so they're not mistaken for tokens -if [ -z $G2P_DICT ]; then - if [ ! -f $LJSPEECH_BASE/cmudict.dict ]; then - echo "Downloading CMU dict." - wget -P $LJSPEECH_BASE $CMUDICT_URL - fi - if [ ! -f $LJSPEECH_BASE/uncommented_cmudict.dict ]; then - echo "Creating uncommented version of CMUdict." - sed 's/\ \#.*//' $LJSPEECH_BASE/cmudict.dict > $LJSPEECH_BASE/uncommented_cmudict.dict - fi - G2P_DICT=$LJSPEECH_BASE/uncommented_cmudict.dict -fi - -# Run alignment -echo "Starting MFA with dictionary at: $G2P_DICT" - -read -r -d '' MFA_ERROR_MSG << EOM -Could not run MFA. If it could not find OpenBlas, you may need to install it manually. -(e.g. sudo apt-get install libopenblas-dev) -EOM - -if ! mfa model download acoustic english; then - echo $MFA_ERROR_MSG -fi -mfa align --clean $LJSPEECH_BASE $G2P_DICT english $LJSPEECH_BASE/alignments - -# Create JSON mappings from word to phonemes and phonemes to indices -echo "Creating word->phone and phone->idx mappings at $LJSPEECH_BASE/mappings.json..." -python create_token2idx_dict.py \ - --dictionary=$G2P_DICT \ - --dict_out=$LJSPEECH_BASE/mappings.json - -# Calculate phoneme durations -echo "Calculating phoneme durations..." -python calculate_durs.py \ - --ljspeech_dir=$LJSPEECH_BASE \ - --mappings=$LJSPEECH_BASE/mappings.json \ - --sr=$SAMPLE_RATE \ - --hop_length=$WINDOW_STRIDE -echo "Phoneme durations and tokens written." diff --git a/scripts/dataset_processing/ljspeech/get_lj_speech_data.py b/scripts/dataset_processing/ljspeech/get_lj_speech_data.py deleted file mode 100644 index b72b0d6d228d..000000000000 --- a/scripts/dataset_processing/ljspeech/get_lj_speech_data.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# USAGE: python get_lj_speech_data.py --data_root= -import argparse -import logging -import os -import tarfile -import urllib.request - -URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2" - - -def get_args(): - parser = argparse.ArgumentParser(description='LJSpeech Data download') - parser.add_argument("--data_root", required=True, type=str) - return parser.parse_args() - - -def __maybe_download_file(destination: str): - """ - Downloads source to destination if it doesn't exist. - If exists, skips download - Args: - destination: local filepath - source: url of resource - Returns: - """ - if not os.path.exists(destination): - logging.info("{0} does not exist. Downloading ...".format(destination)) - urllib.request.urlretrieve(URL, filename=destination + '.tmp') - os.rename(destination + '.tmp', destination) - logging.info("Downloaded {0}.".format(destination)) - else: - logging.info("Destination {0} exists. Skipping.".format(destination)) - return destination - - -def __extract_file(filepath: str, data_dir: str): - try: - tar = tarfile.open(filepath) - tar.extractall(data_dir) - tar.close() - except Exception: - logging.info("Not extracting. Maybe already there?") - - -def main(data_root): - logging.info("\n\nWorking on LJSpeech") - filepath = os.path.join(data_root, "LJSpeech-1.1.tar.bz2") - logging.info("Getting LJSpeech") - __maybe_download_file(filepath) - logging.info("Extracting LJSpeech") - __extract_file(filepath, data_root) - logging.info('Done!') - - -if __name__ == "__main__": - args = get_args() - main(args.data_root) diff --git a/scripts/dataset_processing/tts/extract_sup_data.py b/scripts/dataset_processing/tts/extract_sup_data.py index 13b6b4746c3c..57fa220a733c 100644 --- a/scripts/dataset_processing/tts/extract_sup_data.py +++ b/scripts/dataset_processing/tts/extract_sup_data.py @@ -69,7 +69,10 @@ def preprocess_ds_for_mixer_tts_x(dataloader): def main(cfg): dataset = instantiate(cfg.dataset) dataloader = torch.utils.data.DataLoader( - dataset=dataset, batch_size=1, collate_fn=dataset._collate_fn, num_workers=cfg.dataloader_params.num_workers + dataset=dataset, + batch_size=1, + collate_fn=dataset._collate_fn, + num_workers=cfg.get("dataloader_params", {}).get("num_workers", 4), ) print(f"Processing {cfg.manifest_filepath}:") diff --git a/scripts/dataset_processing/tts/hui_acg/ds_conf/ds_for_fastpitch_align.yaml b/scripts/dataset_processing/tts/hui_acg/ds_conf/ds_for_fastpitch_align.yaml index ba7a54ae82ea..e8c0b58bb418 100644 --- a/scripts/dataset_processing/tts/hui_acg/ds_conf/ds_for_fastpitch_align.yaml +++ b/scripts/dataset_processing/tts/hui_acg/ds_conf/ds_for_fastpitch_align.yaml @@ -24,7 +24,7 @@ dataset: trim: false pitch_fmin: 65.40639132514966 pitch_fmax: 2093.004522404789 - use_beta_binomial_interpolator: true + use_beta_binomial_interpolator: false text_normalizer: _target_: nemo_text_processing.text_normalization.normalize.Normalizer @@ -42,7 +42,6 @@ dataset: punct: true apostrophe: true pad_with_space: true - phonemes: true dataloader_params: num_workers: 12 diff --git a/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_fastpitch_align.yaml b/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_fastpitch_align.yaml index 54a47936a53e..bed6b2ee49af 100644 --- a/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_fastpitch_align.yaml +++ b/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_fastpitch_align.yaml @@ -4,8 +4,8 @@ manifest_filepath: "train_manifest.json" sup_data_path: "sup_data" sup_data_types: [ "align_prior_matrix", "pitch" ] whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" -heteronyms_path: "scripts/tts_dataset_files/heteronyms-030921" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" dataset: _target_: nemo.collections.tts.torch.data.TTSDataset diff --git a/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_mixer_tts.yaml b/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_mixer_tts.yaml index b8f2e7a958db..d4151e888ae0 100644 --- a/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_mixer_tts.yaml +++ b/scripts/dataset_processing/tts/ljspeech/ds_conf/ds_for_mixer_tts.yaml @@ -4,8 +4,8 @@ manifest_filepath: "train_manifest.json" sup_data_path: "sup_data" sup_data_types: [ "align_prior_matrix", "pitch" ] whitelist_path: "nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv" -phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.07" -heteronyms_path: "scripts/tts_dataset_files/heteronyms-030921" +phoneme_dict_path: "scripts/tts_dataset_files/cmudict-0.7b_nv22.08" +heteronyms_path: "scripts/tts_dataset_files/heteronyms-052722" dataset: _target_: nemo.collections.tts.torch.data.TTSDataset diff --git a/scripts/tts_dataset_files/cmudict-0.7b_nv22.07 b/scripts/tts_dataset_files/cmudict-0.7b_nv22.08 similarity index 99% rename from scripts/tts_dataset_files/cmudict-0.7b_nv22.07 rename to scripts/tts_dataset_files/cmudict-0.7b_nv22.08 index cba9c40f2da2..1c18eb3c6aa3 100644 --- a/scripts/tts_dataset_files/cmudict-0.7b_nv22.07 +++ b/scripts/tts_dataset_files/cmudict-0.7b_nv22.08 @@ -51,6 +51,9 @@ ;;; - comments (like this section) are allowed ;;; - file name is major version; vers/rev information is now in the header ;;; +;;; [20220817] Merged with more recent version, entries added +;;; - Merged with https://github.com/Alexir/CMUdict/blob/master/cmudict-0.7b +;;; - British spellings omitted ;;; ;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;; @@ -143,6 +146,7 @@ AARDEMA AA0 R D EH1 M AH0 AARDVARK AA1 R D V AA2 R K AARDVARKS AA1 R D V AA2 R K S AARGH AA1 R G +AARHUS AA2 HH UW1 S AARON EH1 R AH0 N AARON'S EH1 R AH0 N Z AARONS EH1 R AH0 N Z @@ -189,6 +193,7 @@ ABATEMENT AH0 B EY1 T M AH0 N T ABATEMENTS AH0 B EY1 T M AH0 N T S ABATES AH0 B EY1 T S ABATING AH0 B EY1 T IH0 NG +ABATTOIR AE2 B AH0 T W AA1 R ABBA AE1 B AH0 ABBADO AH0 B AA1 D OW0 ABBAS AH0 B AA1 S @@ -225,6 +230,7 @@ ABC'S EY1 B IY2 S IY2 Z ABCO AE1 B K OW0 ABCOTEK AE1 B K OW0 T EH2 K ABCS EY1 B IY2 S IY2 Z +ABD EY2 B IY2 D IY1 ABDALLA AE2 B D AE1 L AH0 ABDALLAH AE2 B D AE1 L AH0 ABDEL AE1 B D EH2 L @@ -340,6 +346,8 @@ ABKHAZIAN(2) AE0 B K AA1 Z Y AH0 N ABKHAZIAN(3) AE0 B K AE1 Z Y AH0 N ABKHAZIANS AE0 B K AA1 Z IY0 AH0 N Z ABKHAZIANS(1) AE0 B K AE1 Z IY0 AH0 N Z +ABLATE AH2 B L EY1 T +ABLATION AH2 B L EY1 SH AH0 N ABLAZE AH0 B L EY1 Z ABLE EY1 B AH0 L ABLE-BODIED EY1 B AH0 L B AA1 D IY0 D @@ -350,6 +358,8 @@ ABLES EY1 B AH0 L Z ABLEST EY1 B AH0 L S T ABLEST(1) EY1 B L AH0 S T ABLOOM AH0 B L UW1 M +ABLUTION AH0 B L UW1 SH AH0 N +ABLUTIONS AH0 B L UW1 SH AH0 N Z ABLY EY1 B L IY0 ABNEGATION AE2 B N EH0 G EY1 SH AH0 N ABNER AE1 B N ER0 @@ -411,6 +421,9 @@ ABOVEBOARD AH0 B AH1 V B AO2 R D ABPLANALP AE1 B P L AH0 N AE0 L P ABRA AA1 B R AH0 ABRACADABRA AE2 B R AH0 K AH0 D AE1 B R AH0 +ABRADE AE0 B R EY1 D +ABRADED AE0 B R EY1 D IH0 D +ABRADES AE0 B R EY1 D Z ABRAHAM EY1 B R AH0 HH AE2 M ABRAHAMIAN AE2 B R AH0 HH EY1 M IY0 AH0 N ABRAHAMS EY1 B R AH0 HH AE2 M Z @@ -587,6 +600,7 @@ ACAMPSIA AH0 K AE1 M P S Y AH0 ACANTHA AA0 K AA1 N DH AH0 ACAPULCO AE2 K AH0 P UH1 L K OW0 ACARY AE1 K ER0 IY0 +ACC AY2 S IY2 S IY1 ACCARDI AA0 K AA1 R D IY0 ACCARDO AA0 K AA1 R D OW0 ACCEDE AE0 K S IY1 D @@ -757,6 +771,7 @@ ACCURATELY AE1 K Y ER0 AH0 T L IY0 ACCURAY AE1 K Y ER0 EY2 ACCURAY'S AE1 K Y ER0 EY2 Z ACCURIDE AE1 K Y ER0 AY2 D +ACCURSED AE2 K ER1 S T ACCURSO AA0 K UH1 R S OW0 ACCUSATION AE2 K Y AH0 Z EY1 SH AH0 N ACCUSATION(1) AE2 K Y UW0 Z EY1 SH AH0 N @@ -806,6 +821,8 @@ ACHED EY1 K T ACHEE AH0 CH IY1 ACHENBACH AE1 K IH0 N B AA0 K ACHENBAUM AE1 K AH0 N B AW2 M +ACHENE AH0 K IY1 N +ACHENES AH0 K IY1 N Z ACHES EY1 K S ACHESON AE1 CH AH0 S AH0 N ACHESON'S AE1 CH AH0 S AH0 N Z @@ -836,17 +853,17 @@ ACHORN AE1 K ER0 N ACHTENBERG AE1 K T EH0 N B ER0 G ACHTERBERG AE1 K T ER0 B ER0 G ACHY EY1 K IY0 -ACID AE1 S AH0 D +ACID AE1 S IH0 D ACIDIC AH0 S IH1 D IH0 K ACIDIFICATION AH0 S IH2 D AH0 F AH0 K EY1 SH AH0 N ACIDIFIED AH0 S IH1 D AH0 F AY2 D ACIDIFIES AH0 S IH1 D AH0 F AY2 Z ACIDIFY AH0 S IH1 D AH0 F AY2 ACIDITY AH0 S IH1 D AH0 T IY0 -ACIDLY AE1 S AH0 D L IY0 -ACIDOSIS AE2 S AH0 D OW1 S AH0 S -ACIDS AE1 S AH0 D Z -ACIDURIA AE2 S AH0 D UH1 R IY0 AH0 +ACIDLY AE1 S IH0 D L IY0 +ACIDOSIS AE2 S IH0 D OW1 S AH0 S +ACIDS AE1 S IH0 D Z +ACIDURIA AE2 S IH0 D UH1 R IY0 AH0 ACIERNO AA0 S IH1 R N OW0 ACK AE1 K ACKER AE1 K ER0 @@ -872,6 +889,7 @@ ACKNOWLEDGEMENTS AE0 K N AA1 L IH0 JH M AH0 N T S ACKNOWLEDGES AE0 K N AA1 L IH0 JH IH0 Z ACKNOWLEDGING AE0 K N AA1 L IH0 JH IH0 NG ACKNOWLEDGMENT AE0 K N AA1 L IH0 JH M AH0 N T +ACKNOWLEDGMENTS AE0 K N AA1 L IH0 JH M AH0 N T S ACKROYD AE1 K R OY2 D ACKROYD'S AE1 K R OY2 D Z ACMAT AE1 K M AE0 T @@ -905,6 +923,7 @@ ACQUAVIVA AA0 K W AA0 V IY1 V AH0 ACQUIESCE AE2 K W IY0 EH1 S ACQUIESCED AE2 K W IY0 EH1 S T ACQUIESCENCE AE2 K W IY0 EH1 S AH0 N S +ACQUIESCENT AE2 K W IY0 EH1 S AH0 N T ACQUIESCING AE2 K W IY0 EH1 S IH0 NG ACQUIRE AH0 K W AY1 ER0 ACQUIRED AH0 K W AY1 ER0 D @@ -949,6 +968,7 @@ ACRYLIC AH0 K R IH1 L IH0 K ACRYLICS AH0 K R IH1 L IH0 K S ACT AE1 K T ACT'S AE1 K T S +ACTA AE1 K T AA0 ACTAVA AE2 K T AA1 V AH0 ACTAVA'S AE2 K T AA1 V AH0 Z ACTAVAS AE2 K T AA1 V AH0 Z @@ -1196,6 +1216,7 @@ ADES EY1 D Z ADEY EY1 D IY0 ADGER AE1 JH ER0 ADHAM AE1 D HH AE0 M +ADHD EY2 D IY2 EY2 CH D IY1 ADHERE AH0 D HH IH1 R ADHERED AE0 D HH IH1 R D ADHERENCE AH0 D HH IH1 R AH0 N S @@ -1383,6 +1404,7 @@ ADREA AA1 D R IY0 AH0 ADRENAL AH0 D R IY1 N AH0 L ADRENALIN AH0 D R EH1 N AH0 L IH0 N ADRENALINE AH0 D R EH1 N AH0 L AH0 N +ADRENERGIC AH0 D R EH0 N EH1 R JH IH0 K ADRIA AA1 D R IY0 AH0 ADRIAN EY1 D R IY0 AH0 N ADRIANA EY2 D R IY0 AE1 N AH0 @@ -1397,6 +1419,8 @@ ADROITLY AH0 D R OY1 T L IY0 ADS AE1 D Z ADS' AE1 D Z ADSIT AE1 D S IH0 T +ADSL EY2 D IY2 EH2 S EH1 L +ADSORPTION AH0 D S AO1 R P SH AH0 N ADSS AE1 D S ADSS(1) EY1 D IY1 EH1 S EH1 S ADSTAR AE1 D S T AA0 R @@ -1468,6 +1492,7 @@ ADVERSE(1) AE1 D V ER2 S ADVERSE(2) AE2 D V ER1 S ADVERSELY AE0 D V ER1 S L IY0 ADVERSITY AE0 D V ER1 S IH0 T IY2 +ADVERT AE1 D V ER0 T ADVERTISE AE1 D V ER0 T AY2 Z ADVERTISED AE1 D V ER0 T AY2 Z D ADVERTISED(1) AE2 D V ER0 T AY1 Z D @@ -1574,6 +1599,7 @@ AERONAUTICS EH2 R AH0 N AO1 T IH0 K S AEROPERU EH2 R OW0 P EY0 R UW1 AEROPERU'S EH2 R OW0 P EY0 R UW1 Z AEROQUIP EH1 R AH0 K W IH2 P +AEROSCIENCE EH2 R OW0 S AY1 AH0 S AEROSMITH EH1 R OW0 S M IH2 TH AEROSMITH'S EH1 R OW0 S M IH2 TH S AEROSOL EH1 R AH0 S AA2 L @@ -1698,6 +1724,7 @@ AFICIONADO AH0 F IY2 SH Y AH0 N AA1 D OW2 AFICIONADOS AH0 F IH2 SH AH0 N AA1 D OW0 Z AFIELD AH0 F IY1 L D AFIRE AH0 F AY1 R +AFL EY2 EH2 F EH1 L AFLAME AH0 F L EY1 M AFLATOXIN AE2 F L AH0 T AA1 K S IH0 N AFLOAT AH0 F L OW1 T @@ -1906,6 +1933,7 @@ AGNOR AE1 G N ER0 AGNOS AE1 G N OW0 S AGNOSIO AE0 G N OW1 S IY0 OW0 AGNOSTIC AE0 G N AA1 S T IH0 K +AGNOSTICALLY AE0 G N AA1 S T IH0 K L IY2 AGNOSTICS AE0 G N AA1 S T IH0 K S AGO AH0 G OW1 AGOG AH0 G AA1 G @@ -1972,6 +2000,7 @@ AGROKOMERC AE1 G R AH0 K OW0 M ER2 K AGRONOMIST AH0 G R AA1 N AH0 M IH0 S T AGRONOMISTS AH0 G R AA1 N AH0 M IH0 S T S AGRONOMISTS(1) AH0 G R AA1 N AH0 M IH0 S +AGRONOMY AH0 G R AA1 N AH0 M IH2 AGROSIAND AH0 G R OW1 S IY0 AH0 N D AGROUND AH0 G R AW1 N D AGRUSA AA0 G R UW1 S AH0 @@ -1983,6 +2012,7 @@ AGUANGA AH0 G W AA1 N G AH0 AGUASCALIENTES AA2 G W AH0 S K AE0 L Y EH1 N T EH0 S AGUAYO AA0 G W EY1 OW0 AGUDELO AA0 G UW0 D EY1 L OW0 +AGUE EY1 G Y UW2 AGUERO AA0 G EH1 R OW0 AGUIAR AA1 G W IY0 ER0 AGUILA AA0 G W IY1 L AH0 @@ -2534,6 +2564,7 @@ ALCOHOL-DRENCHED AE1 L K AH0 HH AA2 L D R EH1 N CH T ALCOHOLIC AE2 L K AH0 HH AA1 L IH0 K ALCOHOLICS AE2 L K AH0 HH AA1 L IH0 K S ALCOHOLISM AE1 L K AH0 HH AO2 L IH2 Z AH0 M +ALCOHOLS AE1 L K AH0 HH AA2 L Z ALCON AH0 L K AA1 N ALCORN AA0 L K AO1 R N ALCORTA AA0 L K AO1 R T AH0 @@ -2547,6 +2578,7 @@ ALDANA AA0 L D AE1 N AH0 ALDAPE AA0 L D AA1 P EY0 ALDAY AE1 L D EY0 ALDEBARAN AE0 L D EH1 B ER0 AH0 N +ALDEBURGH AE1 L D B ER2 G ALDEN AA1 L D AH0 N ALDENVILLE AA1 L D AH0 N V IH0 L ALDER AO1 L D ER0 @@ -2722,6 +2754,7 @@ ALGOMA AE0 L G OW1 M AH0 ALGONQUIAN AE0 L G AA1 NG K IY0 AH0 N ALGONQUIN AE0 L G AA1 NG K W IH0 N ALGORITHM AE1 L G ER0 IH2 DH AH0 M +ALGORITHMIC AE1 L G ER0 IH2 DH AH0 M IH0 K ALGORITHMS AE1 L G ER0 IH2 DH AH0 M Z ALGUIRE AA0 L G W IH1 R EY0 ALGY AE1 L JH IY0 @@ -2903,6 +2936,7 @@ ALLENWOOD AE1 L AH0 N W UH2 D ALLER AO1 L ER0 ALLERGAN AE1 L ER0 JH AH0 N ALLERGEN AE1 L ER0 JH AH0 N +ALLERGENIC AE1 L ER0 JH AH0 N IH0 K ALLERGENS AE1 L ER0 JH AH0 N Z ALLERGIC AH0 L ER1 JH IH0 K ALLERGIES AE1 L ER0 JH IY0 Z @@ -3384,6 +3418,7 @@ ALZHEIMER'S AE1 L Z HH AY2 M ER0 Z ALZHEIMER'S(1) AA1 T S Z HH AY2 M ER0 Z ALZONA AE2 L Z OW1 N AH0 AL_AMEIN AE1 L AH0 M EY2 N +AMA EY2 EH2 M EY1 AMABEL AE1 M AH0 B EH2 L AMABELLE AE1 M AH0 B AH0 L AMABILE AA0 M AA1 B AH0 L @@ -3494,6 +3529,7 @@ AMBERSON AE1 M B ER0 S AH0 N AMBERY AE1 M B ER0 IY0 AMBIANCE AE1 M B IY0 AH0 N S AMBIDEXTROUS AE2 M B IH0 D EH1 K S T R AH0 S +AMBIEN AE1 M B IY0 EH2 N AMBIENCE AE1 M B IY0 AH0 N S AMBIENT AE1 M B IY0 AH0 N T AMBIGUITIES AE0 M B AH0 G Y UW1 AH0 T IY0 Z @@ -3534,6 +3570,8 @@ AMBS AE1 M Z AMBUEHL AE1 M B UH0 L AMBULANCE AE1 M B Y AH0 L AH0 N S AMBULANCES AE1 M B Y AH0 L AH0 N S IH0 Z +AMBULATE AE1 M B Y AH0 L EY2 T +AMBULATOR AE1 M B Y AH0 L EY2 T ER0 AMBULATORY AE1 M B Y AH0 L AH0 T AO2 R IY0 AMBURGEY AE1 M B ER0 G IY0 AMBURN AH0 M B ER1 N @@ -3560,8 +3598,9 @@ AMEDCO AH0 M EH1 D K OW0 AMEDEE AE1 M IH0 D IY0 AMEEN AH0 M IY1 N AMELIA AH0 M IY1 L Y AH0 -AMELINDA AA0 M EH0 L IY1 N D AH0 -AMELINE AA0 M EH0 L IY1 N IY0 +AMELIE AA2 M EH0 L IY1 +AMELINDA AA2 M EH0 L IY1 N D AH0 +AMELINE AA2 M EH0 L IY1 N IY0 AMELIO AH0 M IY1 L IY0 OW0 AMELIORATE AH0 M IY1 L Y ER0 EY2 T AMELIORATED AH0 M IY1 L IY0 ER0 EY2 T IH0 D @@ -3614,6 +3653,7 @@ AMERICARE AH0 M EH1 R IH0 K EH2 R AMERICARES AH0 M EH1 R IH0 K EH2 R Z AMERICAS AH0 M EH1 R IH0 K AH0 Z AMERICAS' AH0 M EH1 R IH0 K AH2 Z +AMERICIUM AH0 M EH0 R IH1 S IY2 AH0 M AMERICO AH0 M ER1 IH0 K OW0 AMERICOLD AH0 M EH1 R IH0 K OW2 L D AMERICORP AH0 M EH1 R IH0 K AO2 R @@ -3659,6 +3699,7 @@ AMFAC AE1 M F AE0 K AMFESCO AE0 M F EH1 S K OW0 AMGEN AE1 M JH EH0 N AMGEN'S AE1 M JH EH0 N Z +AMHARIC AA0 M HH AA1 R IH0 K AMHERST AE1 M ER0 S T AMHERSTDALE AE1 M ER0 S T D EY2 L AMHOIST AE0 M HH OY1 S T @@ -3673,6 +3714,8 @@ AMICO AA0 M IY1 K OW0 AMICONE AE1 M IH0 K OW2 N AMICUS AH0 M IY1 K AH0 S AMID AH0 M IH1 D +AMIDE EY1 M AY2 D +AMIDES EY1 M AY2 D Z AMIDI AA0 M IY1 D IY0 AMIDON AE1 M IH0 D AA0 N AMIDSHIPS AH0 M IH1 D SH IH0 P S @@ -3683,6 +3726,7 @@ AMIGO AH0 M IY1 G OW2 AMIGOS AH0 M IY1 G OW2 Z AMILIA AA0 M IY1 L IY0 AH0 AMIN AA0 M IY1 N +AMINE EY2 M IY1 N AMINO AH0 M IY1 N OW0 AMINTA AH0 M IH1 N T AH0 AMIOT EY1 M IY0 AH0 T @@ -3703,6 +3747,7 @@ AMISON AE1 M IH0 S AH0 N AMISS AH0 M IH1 S AMISTAD AE1 M AH0 S T AE2 D AMIT AA2 M IY1 T +AMITABHA AH0 M IY2 T AA1 B AH0 AMITAI AE1 M IH0 T AY2 AMITY AE1 M IH0 T IY0 AMITYVILLE AE1 M IH0 T IY0 V IH2 L @@ -3967,6 +4012,7 @@ ANATOLI AE2 N AH0 T OW1 L IY0 ANATOLIA AE2 N AH0 T OW1 L IY0 AH0 ANATOLIAN AE2 N AH0 T OW1 L IY0 AH0 N ANATOLY AE2 N AH0 T OW1 L IY0 +ANATOMIC AE2 N AH0 T AA1 M IH0 K ANATOMICAL AE2 N AH0 T AA1 M IH0 K AH0 L ANATOMICALLY AE2 N AH0 T AA1 M AH0 K L IY0 ANATOMIST AH0 N AE1 T AH0 M IH0 S T @@ -4035,6 +4081,7 @@ ANDERT AE1 N D ER0 T ANDERTON AE1 N D ER0 T AH0 N ANDES AE1 N D IY0 Z ANDESITE AE1 N D IH0 S AY2 T +ANDI AE1 N D IY0 ANDIE AE1 N D IY0 ANDING AE1 N D IH0 NG ANDINO AA0 N D IY1 N OW0 @@ -4144,8 +4191,8 @@ ANESTHETIC AE2 N AH0 S TH EH1 T IH0 K ANESTHETICS AE2 N AH0 S TH EH1 T IH0 K S ANESTHETIST AH0 N EH1 S TH EH0 T IH0 S T ANETTE AH0 N EH1 T -ANEURISM AE1 N Y UH0 R IH2 Z AH0 M -ANEURISM(1) AE1 N Y UH0 R IH2 Z M +ANEURYSM AE1 N Y UH0 R IH2 Z AH0 M +ANEURYSM(1) AE1 N Y UH0 R IH2 Z M ANEW AH0 N UW1 ANEW(1) AH0 N Y UW1 ANFAL EY1 EH1 N EH1 F EY1 EH1 L @@ -4257,6 +4304,7 @@ ANGOVE AA0 NG G OW1 V IY0 ANGRIER AE1 NG G R IY0 ER0 ANGRIEST AE1 NG G R IY0 AH0 S T ANGRILY AE1 NG G R AH0 L IY0 +ANGRINESS AE1 NG G R IY0 N EH2 S ANGRY AE1 NG G R IY0 ANGST AA1 NG K S T ANGSTADT AE1 NG SH T AE0 T @@ -4291,10 +4339,12 @@ ANIMATE(1) AE1 N AH0 M EY2 T ANIMATED AE1 N AH0 M EY2 T IH0 D ANIMATES AE1 N AH0 M AH0 T S ANIMATES(1) AE1 N AH0 M EY2 T S +ANIMATING AE2 N AH0 M EY1 T IH0 NG G ANIMATION AE2 N AH0 M EY1 SH AH0 N ANIMATIONS AE2 N AH0 M EY1 SH AH0 N Z ANIMATOR AE1 N AH0 M EY2 T ER0 ANIMATORS AE1 N AH0 M EY2 T ER0 Z +ANIME AE2 N IH0 M EY1 ANIMISM AE1 N AH0 M IH2 Z AH0 M ANIMIST AE1 N AH0 M AH0 S T ANIMISTS AE1 N AH0 M AH0 S T S @@ -4304,6 +4354,9 @@ ANIMOSITY AE2 N AH0 M AA1 S AH0 T IY0 ANIMOUS AE1 N IH0 M AH0 S ANIMUS AE1 N IH0 M AH0 S ANINAT AE1 N IH0 N AE0 T +ANION AE1 N AY2 AO0 N +ANIONS AE1 N AY2 AO0 N Z +ANISA AE0 N IY1 S AH0 ANISE AE1 N AH0 S ANISEED AE1 N AH0 S IY2 D ANISETTE AE2 N AH0 S EH1 T @@ -4320,6 +4373,7 @@ ANKENY AH0 NG K IY1 N IY0 ANKER AE1 NG K ER0 ANKERIUM AE0 NG K ER1 IY0 AH0 M ANKH AE1 N K +ANKITA AE2 N K IY1 T AH0 ANKLAM AE1 NG K L AH0 M ANKLE AE1 NG K AH0 L ANKLEBONE AE1 NG K AH0 L B OW2 N @@ -4373,6 +4427,7 @@ ANNIHILATED AH0 N AY1 AH0 L EY2 T IH0 D ANNIHILATING AH0 N AY1 AH0 L EY2 T IH0 NG ANNIHILATION AH0 N AY2 AH0 L EY1 SH AH0 N ANNIS AE1 N IY0 Z +ANNISSA AE0 N IY1 S AH0 ANNISTON AE1 N IH0 S T IH0 N ANNISTON(1) AE1 N IH0 S IH0 N ANNIVERSARIES AE2 N AH0 V ER1 S ER0 IY0 Z @@ -4432,6 +4487,7 @@ ANOMALIES AH0 N AA1 M AH0 L IY0 Z ANOMALOUS AH0 N AA1 M AH0 L AH0 S ANOMALY AH0 N AA1 M AH0 L IY0 ANOMIE AE1 N AH0 M IY0 +ANON AE2 N AO1 N ANONA AA0 N OW1 N AH0 ANONYMITY AE2 N AH0 N IH1 M IH0 T IY0 ANONYMOUS AH0 N AA1 N AH0 M AH0 S @@ -4445,6 +4501,7 @@ ANOREXICS AE2 N ER0 EH1 K S IH0 K S ANORTHITE AE0 N AO1 R TH AY2 T ANOTHER AH0 N AH1 DH ER0 ANOTHER'S AH0 N AH1 DH ER0 Z +ANOVA AH0 N OW1 V AA0 ANREDER AE1 N R EH2 D ER0 ANRIG AE1 N R IH0 G ANSA AE1 N S AH0 @@ -4492,8 +4549,8 @@ ANSWERED AE1 N S ER0 D ANSWERING AE1 N S ER0 IH0 NG ANSWERS AE1 N S ER0 Z ANT AE1 N T -ANTACID AE0 N T AE1 S AH0 D -ANTACIDS AE0 N T AE1 S AH0 D Z +ANTACID AE0 N T AE1 S IH0 D +ANTACIDS AE0 N T AE1 S IH0 D Z ANTAGONISM AE0 N T AE1 G AH0 N IH2 Z AH0 M ANTAGONISMS AE0 N T AE1 G AH0 N IH2 Z AH0 M Z ANTAGONIST AE0 N T AE1 G AH0 N AH0 S T @@ -4523,6 +4580,7 @@ ANTECEDENT AE2 N T EH1 S AH0 D AH0 N T ANTECEDENT(1) AE2 N T IH0 S IY1 D AH0 N T ANTECEDENTS AE2 N T IH0 S IY1 D AH0 N T S ANTECEDENTS(1) AE2 N T EH1 S AH0 D AH0 N T S +ANTECHAMBER AE1 N T EH0 CH EY2 M B ER0 ANTED AE1 N T IH0 D ANTED(1) AE1 N T IY0 D ANTELL AE0 N T EH1 L @@ -4648,10 +4706,12 @@ ANTILOCK AE1 N T IY0 L AA1 K ANTILOCK(1) AE1 N T AY0 L AA1 K ANTIMATTER AE0 T AY0 M AE1 T ER0 ANTIMISSILE AE2 N T AY2 M IH1 S AH0 L +ANTINOMY AE0 T IH1 N OW0 M IY2 ANTIOCH AE1 N T IY0 AA2 K ANTIOCHUS AE0 N T AY1 AH0 K AH0 S ANTIOXIDANT AE2 N T IY0 AA1 K S AH0 D AH0 N T ANTIOXIDANTS AE2 N T IY0 AA1 K S AH0 D AH0 N T S +ANTIPASTO AE2 N T IY0 P AA1 S T OW0 ANTIPATHIES AE0 N T IH1 P AH0 TH IY0 Z ANTIPATHY AE0 N T IH1 P AH0 TH IY0 ANTIPERSONNEL AE0 N T AY2 P ER0 S AH0 N EH1 L @@ -4693,6 +4753,7 @@ ANTITOXIN AE2 N T IY0 T AA1 K S AH0 N ANTITOXINS AE2 N T IY0 T AA1 K S AH0 N Z ANTITRUST AE2 N T AY0 T R AH1 S T ANTIVIRAL AE2 N T IY0 V AY1 R AH0 L +ANTIVIRUS AE2 N T IY0 V AY1 R AH0 S ANTIWAR AE2 N T AY0 W AO1 R ANTIWAR(1) AE2 N T IY0 W AO1 R ANTKOWIAK AH0 N T K AW1 IY0 AE0 K @@ -4717,6 +4778,7 @@ ANTONIA AE0 N T OW1 N IY0 AH0 ANTONIN AE1 N T AH0 N IH0 N ANTONINI AA0 N T OW0 N IY1 N IY0 ANTONINI'S AA0 N T OW0 N IY1 N IY0 Z +ANTONINO AE2 N T OW0 N IY1 N OW0 ANTONIO AE0 N T OW1 N IY0 OW0 ANTONIO'S AE0 N T OW1 N IY0 OW2 Z ANTONIOS AE0 N T OW1 N IY0 OW2 Z @@ -4746,6 +4808,7 @@ ANTUNEZ AA0 N T UW1 N EH0 Z ANTWERP AE1 N T W ER0 P ANTWINE AE1 N T W AY2 N ANUBIS AH0 N UW1 B IH0 S +ANURADHAPURA AA2 N UW0 R AA1 D AH0 P UW2 R AA0 ANUS EY1 N AH0 S ANVIL AE1 N V AH0 L ANWAR AE1 N W AA0 R @@ -4831,6 +4894,7 @@ APEC'S EY1 P EH2 K S APEL AA0 P EH1 L APELIKE EY1 P L AY2 K APENNINE AE1 P AH0 N IY2 N +APERCU AE1 P ER0 S UW2 APERITIF AH0 P EH2 R AH0 T IY1 F APERTURE AE1 P ER0 CH ER0 APES EY1 P S @@ -4851,6 +4915,7 @@ APHRODITE AE2 F R AH0 D AY1 T IY0 APHRODITE'S AE2 F R AH0 D AY1 T IY0 Z APHRODITES AE2 F R AH0 D AY1 T IY0 Z APIA AA1 P IH0 AA2 +APICAL AE1 P IH0 K AH0 L APICELLA AA2 P IH0 S EH1 L AH0 APIECE AH0 P IY1 S APING EY1 P IH0 NG @@ -4858,6 +4923,7 @@ APLENTY AH0 P L EH1 N T IY0 APLIN AE1 P L IH0 N APLOMB AH0 P L AA1 M APNEA AE1 P N IY0 AH0 +APO EY2 P IY2 OW1 APOCALYPSE AH0 P AA1 K AH0 L IH2 P S APOCALYPTIC AH0 P AA2 K AH0 L IH1 P T IH0 K APOCRYPHAL AH0 P AA1 K R AH0 F AH0 L @@ -4941,6 +5007,8 @@ APPEL AE1 P AH0 L APPELBAUM AE1 P AH0 L B AW2 M APPELHANS AE1 P IH0 L HH AH0 N Z APPELL AE1 P AH0 L +APPELLANT AH0 P EH1 L IH0 N T +APPELLANTS AH0 P EH1 L IH0 N T S APPELLATE AH0 P EH1 L IH0 T APPELLATE(1) AH0 P EH1 L EY2 T APPELLATION AE2 P AH0 L EY1 SH AH0 N @@ -4953,6 +5021,7 @@ APPENDAGES AH0 P EH1 N D IH0 JH IH0 Z APPENDECTOMIES AE2 P AH0 N D EH1 K T AH0 M IY0 Z APPENDECTOMY AE2 P IH0 N D EH1 K T AH0 M IY0 APPENDED AH0 P EH1 N D IH0 D +APPENDICES AH0 P EH1 N D IH0 S IY2 Z APPENDICITIS AH0 P EH2 N D IH2 S AY1 T IH0 Z APPENDIX AH0 P EH1 N D IH0 K S APPENDIXES AH0 P EH1 N D IH0 K S IH0 Z @@ -5185,6 +5254,7 @@ ARAKAWA AA2 R AA0 K AA1 W AH0 ARAKELIAN AE0 R AH0 K EH1 L Y AH0 N ARAKI AA0 R AA1 K IY0 ARAL AA1 R AA0 L +ARAMAIC AA2 R AA0 M EH1 Y IH0 K ARAMBULA AA0 R AA0 M B UW1 L AH0 ARAMCO ER0 AE1 M K OW0 ARAMID EH1 R AH0 M IH0 D @@ -5317,6 +5387,7 @@ ARCHIMEDES AA2 R K AH0 M IY1 D IY0 Z ARCHING AA1 R CH IH0 NG ARCHIPELAGO AA2 R K AH0 P EH1 L AH0 G OW2 ARCHIPELAGO(1) AA2 R CH AH0 P AH0 L EY1 G OW2 +ARCHIPPUS AE2 R K IH1 P AH0 S ARCHITECT AA1 R K AH0 T EH2 K T ARCHITECT'S AA1 R K AH0 T EH2 K T S ARCHITECTS AA1 R K AH0 T EH2 K T S @@ -5330,6 +5401,7 @@ ARCHITRAVE AA1 R K AH0 T R EY2 V ARCHITRAVES AA1 R K AH0 T R EY2 V Z ARCHIVAL AA0 R K AY1 V AH0 L ARCHIVE AA1 R K AY2 V +ARCHIVED AA1 R K AY2 V D ARCHIVES AA1 R K AY2 V Z ARCHIVIST AA1 R K AH0 V IH0 S T ARCHIVIST(1) AA1 R K AY0 V IH0 S T @@ -5476,6 +5548,7 @@ ARGYROPOULOS AA2 R JH IH0 R AA1 P OW0 L AH0 S ARI AA1 R IY0 ARIA AA1 R IY0 AH0 ARIADNE EH2 R IY0 AE1 D N IY0 +ARIAL EH1 R IY2 AH0 L ARIAN AE1 R IY0 AH0 N ARIANA AA0 R IY0 AE1 N AH0 ARIANE EH2 R IY0 AE1 N @@ -5492,6 +5565,7 @@ ARID(1) EH1 R AH0 D ARIDA AH0 R IY1 D AH0 ARIE EH1 R IY0 ARIEL EH1 R IY0 AH0 L +ARIELA AA0 R IY0 EH1 L AH0 ARIELLA AA0 R IY0 EH1 L AH0 ARIES EH1 R IY0 Z ARINGTON AA1 R IH0 NG T AH0 N @@ -5826,6 +5900,7 @@ ARSENIC AA1 R S AH0 N IH0 K ARSENIDE AA1 R S AH0 N AY2 D ARSENIO AA2 R S IY1 N IY0 OW0 ARSES AA1 R S IH0 Z +ARSHIA AA1 R SH Y AH0 ARSLANIAN AA2 R S L EY1 N IY0 AH0 N ARSON AA1 R S AH0 N ARSONIST AA1 R S AH0 N AH0 S T @@ -5871,6 +5946,7 @@ ARTICLE(1) AA1 R T IH0 K AH0 L ARTICLE'S AA1 R T IH0 K AH0 L Z ARTICLES AA1 R T AH0 K AH0 L Z ARTICLES(1) AA1 R T IH0 K AH0 L Z +ARTICULAR AA0 R T IH1 K Y AH0 L ER2 ARTICULATE AA0 R T IH1 K Y AH0 L EY2 T ARTICULATE(1) AA0 R T IH1 K Y AH0 L AH0 T ARTICULATED AA0 R T IH1 K Y AH0 L EY2 T AH0 D @@ -5919,6 +5995,7 @@ ARTY'S AA1 R T IY0 Z ARTZ AA1 R T S ARTZT AA1 R T S T ARUBA ER0 UW1 B AH0 +ARUGULA AA2 R UW1 G UW0 L AH0 ARUM EH1 R AH0 M ARUNACHALAM AA0 R UW2 N AH0 CH AA1 L AH0 M ARUNDEL EH1 R AH0 N D AH0 L @@ -6061,6 +6138,7 @@ ASHMEAD AE1 SH M IY2 D ASHMORE AE1 SH M AO0 R ASHOK AE1 SH AA0 K ASHORE AH0 SH AO1 R +ASHRAM AE1 SH R AA0 M ASHRAWI AE0 SH R AA1 W IY0 ASHTEC AE1 SH T EH0 K ASHTEC'S AE1 SH T EH0 K S @@ -6196,8 +6274,10 @@ ASSAULTED AH0 S AO1 L T IH0 D ASSAULTING AH0 S AO1 L T IH0 NG ASSAULTIVE AH0 S AO1 L T IH0 V ASSAULTS AH0 S AO1 L T S -ASSAY AE1 S IY0 +ASSAY AE1 S EY2 +ASSAY(1) AE1 S IY0 ASSAYER AE0 S EY1 ER0 +ASSAYS AE1 S EY2 Z ASSED AE1 S T ASSELIN AE1 S IH0 L IH0 N ASSELSTINE AE1 S AH0 L S T AY2 N @@ -6288,6 +6368,7 @@ ASSOCIATION(1) AH0 S OW2 SH IY0 EY1 SH AH0 N ASSOCIATION'S AH0 S OW2 SH IY0 EY1 SH AH0 N Z ASSOCIATIONS AH0 S OW2 S IY0 EY1 SH AH0 N Z ASSOCIATIONS(1) AH0 S OW2 SH IY0 EY1 SH AH0 N Z +ASSOCIATIVE AH0 S OW1 SH AH0 T IH2 V ASSOCIES AE1 S AH0 S IY0 Z ASSORT AH0 S AO1 R T ASSORTED AH0 S AO1 R T IH0 D @@ -6326,6 +6407,7 @@ ASTHMA AE1 Z M AH0 ASTHMATIC AE0 Z M AE1 T IH0 K ASTHMATICS EH0 S TH M EH1 T IH0 K S ASTIGMATISM AH0 S T IH1 G M AH0 T IH2 Z AH0 M +ASTILBE AH0 S T IH1 B IY2 ASTIN AH0 S T IH1 N ASTLE AE1 S AH0 L ASTLEY AE1 S T L IY0 @@ -6390,6 +6472,7 @@ ASUNCION AH0 S AH1 N SH AH0 N ASUNDER AH0 S AH1 N D ER0 ASWIN AH0 S W IH1 N ASYLUM AH0 S AY1 L AH0 M +ASYMMETRIC EY2 S AH0 M EH1 T R IH0 K ASYMMETRICAL EY2 S AH0 M EH1 T R IH0 K AH0 L ASYMMETRIES EY2 S IH1 M AH0 T R IY0 Z ASYMMETRY EY2 S IH1 M AH0 T R IY0 @@ -6462,6 +6545,7 @@ ATHLETICISM AE0 TH L EH1 T IH0 S IH2 Z M ATHLETICS AE0 TH L EH1 T IH0 K S ATHLONE AE0 TH L OW1 N ATHWART AH0 TH W AO1 R T +ATI EY2 T IY2 AY1 ATICO AE1 T IH0 K OW2 ATIENZA AA0 T IY1 N Z AH0 ATILANO AA0 T IY0 L AA1 N OW0 @@ -6499,6 +6583,7 @@ ATLASES AE1 T L AH0 S IH0 Z ATLER AE1 T L ER0 ATLEY AE1 T L IY0 ATM EY1 T IY2 EH1 M +ATMAN AE1 T M AH0 N ATMEL AE1 T M AH0 L ATMOSPHERE AE1 T M AH0 S F IH2 R ATMOSPHERIC AE2 T M AH0 S F EH1 R IH0 K @@ -6588,6 +6673,7 @@ ATTENTIVENESS AH0 T EH1 N T IH0 V N AH0 S ATTENUATE AH0 T EH1 N Y UW0 EY2 T ATTENUATED AH0 T EH1 N Y UW0 EY2 T IH0 D ATTENUATES AH0 T EH1 N Y UW0 EY2 T S +ATTENUATION AH0 T EH2 N Y UW0 EY1 SH AH0 N ATTERBERRY AE1 T ER0 B EH0 R IY0 ATTERBURY AE1 T ER0 B EH2 R IY0 ATTERMANN AE1 T ER0 M AH0 N @@ -6650,6 +6736,8 @@ AU OW1 AUBE AO1 B AUBEL AW1 B AH0 L AUBER AO1 B ER0 +AUBERGINE AO1 B ER0 JH IY2 N +AUBERGINES AO1 B ER0 JH IY2 N Z AUBERRY AO1 B EH2 R IY0 AUBERT AO1 B ER0 T AUBIN AO1 B IH0 N @@ -6818,6 +6906,7 @@ AURAND AO1 R AH0 N D AUREA AW0 R EY1 AA0 AURELIO AW0 R EY1 L IY0 OW0 AUREOLE AA1 R IY0 OW0 L +AUREUS AO1 R EH2 AH0 S AURIA AO1 R IY0 AH0 AURICH AW1 R IH0 K AURIEMMA AO0 R IY1 M AH0 @@ -7189,6 +7278,7 @@ AWEIDA(1) AH0 W AY1 D AH0 AWESOME AO1 S AH0 M AWESOMELY AA1 S AH0 M L IY0 AWESOMELY(1) AO1 S AH0 M L IY0 +AWESOMENESS AO1 S AH0 M N EH2 S AWESTRUCK AA1 S T R AH2 K AWFUL AO1 F AH0 L AWFULLY AO1 F AH0 L IY0 @@ -7356,6 +7446,7 @@ B-J B IY1 JH EY1 B-J'S B IY1 JH EY1 Z B. B IY1 B.'S B IY1 Z +B.C. B IY2 S IY1 B.S B IY1 Z BA B IY2 EY1 BA(1) B AA1 @@ -7449,6 +7540,7 @@ BABYLONIAN B AE2 B AH0 L OW1 N IY0 AH0 N BABYLONIANS B AE2 B AH0 L OW1 N IY0 AH0 N Z BABYSAT B EY1 B IY0 S AE2 T BABYSIT B EY1 B IY0 S IH0 T +BABYSITS B EY1 B IY0 S IH0 T S BABYSITTER B EY1 B IY0 S IH2 T ER0 BABYSITTERS B EY1 B IY0 S IH2 T ER0 Z BABYSITTING B EY1 B IY0 S IH2 T IH0 NG @@ -7589,6 +7681,8 @@ BACKSTAIRS B AE1 K S T EH2 R Z BACKSTITCH B AE1 K S T IH0 CH BACKSTITCHES B AE1 K S T IH0 CH AH0 Z BACKSTOP B AE1 K S T AA2 P +BACKSTREET B AE1 K S T IY2 T +BACKSTREETS B AE1 K S T IY2 T S BACKSTROKE B AE1 K S T R OW2 K BACKSTROM B AE1 K S T R AH0 M BACKTRACK B AE1 K T R AE2 K @@ -7750,6 +7844,7 @@ BAHM B AE1 M BAHMAN B AA1 M AH0 N BAHN B AE1 N BAHNER B AA1 N ER0 +BAHNHOF B AA2 N HH AO1 F BAHNSEN B AA1 N S AH0 N BAHR B EH1 R BAHR(1) B AA1 R @@ -7877,7 +7972,10 @@ BAKULA B AH0 K UW1 L AH0 BAL B AE1 L BALA B AA1 L AH0 BALABAN B AA0 L AA0 B AA1 N +BALACLAVA B AA2 L AA0 K L AA1 V AA0 +BALACLAVAS B AA2 L AA0 K L AA1 V AA0 Z BALAGUER B AE1 L AH0 G ER0 +BALAK B AA2 L AH0 K BALAKUMAR B AA2 L AH0 K UW0 M AA1 R BALAN B EY1 L AH0 N BALANCE B AE1 L AH0 N S @@ -8090,6 +8188,7 @@ BALONEY B AH0 L OW1 N IY0 BALOW B AE1 L OW0 BALSA B AO1 L S AH0 BALSAM B AO1 L S AH0 M +BALSAMIC B AA2 L S AA1 M IH0 K BALSAMO B AA0 L S AA1 M OW0 BALSBAUGH B AO1 L Z B AO2 BALSER B EY1 L S ER0 @@ -8122,6 +8221,7 @@ BALUJA B AH0 L UW1 JH AH0 BALUKAS B AH0 L UW1 K AH0 Z BALYEAT B AE2 L IY0 AE1 T BALZ B AO1 L Z +BALZAC B AA0 L Z AE1 K BALZANO B AA0 L Z AA1 N OW0 BALZARINI B AA0 L Z AA0 R IY1 N IY0 BALZER B EY1 L Z ER0 @@ -8249,6 +8349,7 @@ BANGISH B AE1 NG IH0 SH BANGKOK B AE0 NG K AA1 K BANGKOK(1) B AE1 NG K AA0 K BANGKOK'S B AE1 NG K AA0 K S +BANGLA B AE1 NG L AA2 BANGLADESH B AE1 NG L AH0 D EH2 SH BANGLADESH'S B AE1 NG L AH0 D EH2 SH IH0 Z BANGLADESHI B AE1 NG L AH0 D EH2 SH IY0 @@ -8970,6 +9071,7 @@ BASHERS B AE1 SH ER0 Z BASHES B AE1 SH IH0 Z BASHFORD B AE1 SH F ER0 D BASHFUL B AE1 SH F AH0 L +BASHFULNESS B AE1 SH F AH0 L N EH2 S BASHING B AE1 SH IH0 NG BASHIR B AH0 SH IH1 R BASHOR B AE1 SH ER0 @@ -9055,6 +9157,7 @@ BASSLER B AE1 S L ER0 BASSMAN B AE1 S M AH0 N BASSO B AE1 S OW0 BASSOON B AH0 S UW1 N +BASSOONIST B AH0 S UW1 N IH0 S T BAST B AE1 S T BASTA B AE1 S T AH0 BASTARACHE B AA0 S T AA1 R EY0 K @@ -9339,6 +9442,7 @@ BAYER'S B EY1 ER0 Z BAYERISCHE B EY2 ER0 IY1 SH BAYERS B EY1 ER0 Z BAYES B EY1 Z +BAYESIAN B EY1 ZH IH0 N BAYH B EY1 BAYING B EY1 IH0 NG BAYLE B EY1 L @@ -9398,6 +9502,7 @@ BBC B IY2 B IY0 S IY1 BBC'S B IY2 B IY0 S IY1 S BBQ B IY1 B IY0 K Y UW2 BBQ(1) B AA1 R B IH0 K Y UW2 +BC B IY2 S IY1 BE B IY1 BE(1) B IY0 BE'S B IY1 Z @@ -9432,6 +9537,7 @@ BEACON B IY1 K AH0 N BEACONS B IY1 K AH0 N Z BEAD B IY1 D BEADED B IY1 D IH0 D +BEADING B IY1 D IH0 NG BEADLE B IY1 D AH0 L BEADLES B IY1 D AH0 L Z BEADLING B IY1 D L IH0 NG @@ -9700,6 +9806,7 @@ BEDGOOD B EH1 D G UH2 D BEDIENT B IY1 D Y IH0 N T BEDINGER B EH1 D IH0 NG ER0 BEDINGFIELD B EH1 D IH0 NG F IY2 L D +BEDIZEN B IH2 D IY1 Z AH0 N BEDKE B EH1 D K IY0 BEDLAM B EH1 D L AH0 M BEDLINGTON B EH1 D L IH0 NG T AH0 N @@ -10007,6 +10114,7 @@ BEITZ B IY1 T S BEITZEL B AY1 T Z AH0 L BEJAR B EY0 Y AA1 R BEJARANO B EY0 Y AA0 R AA1 N OW0 +BEJI B AE1 ZH IY2 BEKAA B EH0 K AA1 BEKAA(1) B AH0 K AA1 BEKAERT B AH0 K EH1 R T @@ -10925,10 +11033,12 @@ BESLER B EH1 S AH0 L ER0 BESLER(1) B EH1 S L ER0 BESNER B EH1 S N ER0 BESNER'S B EH1 S N ER0 Z +BESOTTED B IH0 S AO1 T IH0 D BESPEAK B IH0 S P IY1 K BESPEAKS B IH0 S P IY1 K S BESPECTACLE B IH0 S P EH1 K T AH0 K AH0 L BESPECTACLED B IH0 S P EH1 K T AH0 K AH0 L D +BESPOKE B UH0 S P OW1 K BESS B EH1 S BESSE B EH1 S BESSEMER B EH1 S AH0 M ER0 @@ -11144,7 +11254,9 @@ BEZOLD B EH1 Z OW0 L D BHAGWAN B AA1 G W AA0 N BHAKTA B AA1 K T AH0 BHANGRA B AA1 NG G R AH0 +BHARAT B AA1 R AA2 T BHARATA B AA2 R AA1 T AH0 +BHARATH B AA2 R AA1 T BHATIA B AA1 SH AH0 BHATIA(1) B AA1 T Y AH0 BHATT B AE1 T @@ -11217,6 +11329,8 @@ BIBLER(1) B AY1 B L ER0 BIBLES B AY1 B AH0 L Z BIBLICAL B IH1 B L AH0 K AH0 L BIBLICAL(1) B IH1 B L IH0 K AH0 L +BIBLIOGRAPHIC B IH2 B L IY0 AA1 G R AA2 F IH0 K +BIBLIOGRAPHICAL B IH2 B L IY0 AA0 G R AA1 F IH0 K AH0 L BIBLIOGRAPHIES B IH2 B L IY0 AA1 G R AH0 F IY0 Z BIBLIOGRAPHY B IH2 B L IY0 AA1 G R AH0 F IY0 BIBS B IH1 B Z @@ -11230,6 +11344,7 @@ BICEP B AY1 S EH2 P BICEPS B AY1 S EH2 P S BICHLER B IH1 K AH0 L ER0 BICHLER(1) B IH1 K L ER0 +BICHON B IY2 SH AO1 N BICHSEL B IH1 K S AH0 L BICK B IH1 K BICKEL B IH1 K AH0 L @@ -11671,10 +11786,12 @@ BIONDO B IY0 OW1 N D OW0 BIONDOLILLO B IY0 OW0 N D OW0 L IH1 L OW0 BIONETIC B AY2 OW0 N EH1 T IH0 K BIONETICS B AY2 OW0 N EH1 T IH0 K S +BIONIC B AY2 AO1 N IH0 K BIOPHARM B AY1 AH0 F AA0 R M BIOPHARMACEUTICAL B AY2 OW0 F AA2 R M AH0 S UW1 T IH0 K AH0 L BIOPHARMACY B AY2 OW0 F AA1 R M AH0 S IY0 BIOPHYSICS B AY2 OW0 F IH1 S IH0 K S +BIOPIC B AY1 OW0 P IH2 K BIOPSIES B AY1 AA0 P S IY0 Z BIOPSY B AY1 AA0 P S IY0 BIOS B AY1 OW0 S @@ -11690,6 +11807,9 @@ BIOSPHERE'S B AY1 OW0 S F IH2 R Z BIOSPHERES B AY1 OW0 S F IH2 R Z BIOSPHERIAN B AY2 OW0 S F IH1 R IY0 AH0 N BIOSPHERIANS B AY2 OW0 S F IH1 R IY0 AH0 N Z +BIOSTATISTICIAN B AY2 OW0 S T AA0 T IH0 S T IH1 SH AH0 N +BIOSTATISTICS B AY2 OW0 S T AA0 T IH1 S T IH2 K S +BIOSYNTHESIS B AY2 OW0 S IH1 N TH EH0 S IH0 S BIOSYS B AY1 OW0 S IH0 S BIOSYSTEM B AY1 OW0 S IH2 S T AH0 M BIOSYSTEMS B AY1 OW0 S IH2 S T AH0 M Z @@ -11861,6 +11981,7 @@ BISMUTH B IH1 Z M AH0 TH BISON B AY1 S AH0 N BISPING B IH1 S P IH0 NG BISQUE B IH1 S K +BISQUIT B IH1 S K IH0 T BISS B IH1 S BISSELL B IH1 S AH0 L BISSEN B IH1 S AH0 N @@ -11944,6 +12065,7 @@ BIZARRE B AH0 Z AA1 R BIZARRE(1) B IH0 Z AA1 R BIZET B IH0 Z EH1 T BIZMART B IH1 Z M AA2 R T +BIZRATE B IH1 Z EY2 T BIZUB B IH1 Z AH0 B BIZZARO B IH0 Z AA1 R OW0 BIZZELL B IH1 Z AH0 L @@ -11958,6 +12080,10 @@ BJORKLUND B Y AO1 R K L AH0 N D BJORKMAN B Y AO1 R K M AH0 N BJORN B Y AO1 R N BJORNSTAD B Y AO1 R N S T AH0 D +BLAB B L AE1 B +BLABBED B L AE1 B D +BLABBER B L AE1 B ER0 +BLABBERS B L AE1 B ER0 Z BLACHLY B L AA1 CH L IY0 BLACHLY(1) B L AA1 K L IY0 BLACK B L AE1 K @@ -12097,6 +12223,7 @@ BLANCHETT B L AE1 N CH IH0 T BLANCHETTE B L AH0 N SH EH1 T BLANCHFIELD B L AE1 N CH F IY2 L D BLANCK B L AE1 NG K +BLANCMANGE B L AH0 M AA1 N JH BLANCO B L AE1 NG K OW0 BLAND B L AE1 N D BLANDA B L AE1 N D AH0 @@ -12128,12 +12255,15 @@ BLANKET(1) B L AE1 NG K IH0 T BLANKETED B L AE1 NG K AH0 T IH0 D BLANKETING B L AE1 NG K AH0 T IH0 NG BLANKETS B L AE1 NG K AH0 T S +BLANKIE B L AE1 NG K IY2 +BLANKIES B L AE1 NG K IY2 Z BLANKING B L AE1 NG K IH0 NG BLANKINSHIP B L AE1 NG K IH0 N SH IH0 P BLANKLEY B L AE1 NG K L IY0 BLANKLY B L AE1 NG K L IY0 BLANKLY'S B L AE1 NG K L IY0 Z BLANKS B L AE1 NG K S +BLANKY B L AE1 NG K IY2 BLANN B L AE1 N BLANQUITA B L AA0 N K IY1 T AH0 BLANSETT B L AE1 N S IH0 T @@ -12158,6 +12288,7 @@ BLASIER'S(1) B L EY1 ZH ER0 Z BLASING B L EY1 Z IH0 NG BLASINGAME B L AA0 S IH0 NG G AA1 M IY0 BLASINI B L AH0 S IY1 N IY0 +BLASIO B L AE1 Z IY0 OW2 BLASIUS B L EY1 S IY0 IH0 S BLASKE B L EY1 S K BLASKO B L AA1 S K OW0 @@ -12313,6 +12444,7 @@ BLINDSIDE B L AY1 N D S AY2 D BLINDSIDED B L AY1 N D S AY2 D IH0 D BLINK B L IH1 NG K BLINKED B L IH1 NG K T +BLINKEN B L IH1 NG K AH0 N BLINKING B L IH1 NG K IH0 NG BLINKS B L IH1 NG K S BLINN B L IH1 N @@ -12511,6 +12643,7 @@ BLOWIER B L OW1 IY0 ER0 BLOWIEST B L OW1 IY0 AH0 S T BLOWING B L OW1 IH0 NG BLOWJOB B L OW1 JH AA2 B +BLOWJOBS B L OW1 JH AA2 B Z BLOWN B L OW1 N BLOWOUT B L OW1 AW2 T BLOWOUTS B L OW1 AW2 T S @@ -12555,6 +12688,7 @@ BLUEFIELD B L UW1 F IY2 L D BLUEGRASS B L UW1 G R AE2 S BLUEING B L UW1 IH0 NG BLUEISH B L UW1 IH0 SH +BLUEJACKET B L UW1 JH AE2 K IH0 T BLUEJAY B L UW1 JH EY2 BLUEJEANS B L UW1 JH IY0 N Z BLUELAW B L UW1 L AA2 @@ -12994,6 +13128,7 @@ BOHNERT B OW1 N ER0 T BOHNET B AA1 N IH0 T BOHNHOFF B OW1 N HH AO2 F BOHNING B AA1 N IH0 NG +BOHNOMIE B AO1 N AO0 M IY2 BOHNSACK B OW1 N S AH0 K BOHON B OW1 HH AH0 N BOHR B AO1 R @@ -13033,6 +13168,7 @@ BOISVERT B W AA0 V ER1 T BOITANO B OY2 T AA1 N OW0 BOITNOTT B OY0 T N AA1 T BOIVIN B OY0 V AE1 N +BOJANGLES B OW0 JH AE1 NG G AH0 L Z BOJANGLES' B OW0 JH AE1 NG G AH0 L Z BOJANOWSKI B AH0 Y AH0 N AO1 F S K IY0 BOJARSKI B AH0 Y AA1 R S K IY0 @@ -13127,8 +13263,10 @@ BOLLING B OW1 L IH0 NG BOLLINGER B AA1 L IH0 NG ER0 BOLLMAN B AA1 L M AH0 N BOLLMANN B AA1 L M AH0 N +BOLLOCKS B AO1 L AO0 K S BOLLORE B AA1 L AO0 R BOLLS B OW1 L Z +BOLLY B AO1 L IY2 BOLLYWOOD B AO1 L IH0 W UH2 D BOLLYWOOD'S B AO1 L IH0 W UH2 D Z BOLOGNA B AH0 L OW1 N IY0 @@ -13653,6 +13791,8 @@ BORSCHT B AO1 R SH T BORSE B AO1 R S BORSETH B AO1 R S IH0 TH BORSKI B AO1 R S K IY0 +BORSOD B AO2 R S AO1 D +BORSODI B AO2 R S AO1 D IY2 BORST B AO1 R S T BORSUK B AO1 R S AH0 K BORT B AO1 R T @@ -13834,6 +13974,7 @@ BOTTORFF B AA1 T ER0 F BOTTRELL B AA1 T R AH0 L BOTTS B AA1 T S BOTULISM B AA1 CH UW0 L IH2 Z AH0 M +BOTVINICK B AO1 T V IH0 N IH0 K BOTZ B AA1 T S BOUCH B AW1 CH BOUCHARD B UW0 SH AA1 R D @@ -13877,6 +14018,7 @@ BOULAY B UW0 L EY1 BOULDEN B UH1 D AH0 N BOULDER B OW1 L D ER0 BOULDERS B OW1 L D ER0 Z +BOULDING B OW1 L D IH2 NG BOULE B UW1 L BOULER B AW1 L ER0 BOULET B UW0 L EH1 T @@ -14140,6 +14282,8 @@ BOYSON B OY1 Z AH0 N BOYT B OY1 T BOYTE B OY1 T BOYTER B OY1 T ER0 +BOYTOY B OY1 T OY2 +BOYTOYS B OY1 T OY2 Z BOYUM B OY0 AH1 M BOYZ B OY1 Z BOZA B OW1 Z AH0 @@ -14163,6 +14307,7 @@ BOZTEPE(3) B OW0 Z T EH1 P IY0 BOZZA B AA1 Z AH0 BOZZI B AA1 Z IY0 BOZZO B AA1 Z OW0 +BP B IY2 P IY1 BRA B R AA1 BRAAKSMA B R AA1 K S M AH0 BRAASCH B R AA1 SH @@ -14285,6 +14430,7 @@ BRAGGS B R AE1 G Z BRAGS B R AE1 G Z BRAHAM B R AE1 HH AH0 M BRAHM B R AA1 M +BRAHMAN B R AA1 M AH0 N BRAHMIN B R AA1 M IH0 N BRAHMS B R AA1 M Z BRAHMS'S B R AA1 M Z IH0 Z @@ -14703,6 +14849,7 @@ BREDESON B R EH1 D IH0 S AH0 N BREE B R IY1 BREECE B R IY1 S BREECH B R IY1 CH +BREECHES B R IY1 CH IH0 Z BREECHING B R IY1 CH IH0 NG BREED B R IY1 D BREED'S B R IY1 D Z @@ -14997,6 +15144,7 @@ BRIGANCE B R IH1 G AH0 N S BRIGANDI B R IH0 G AE1 N D IY0 BRIGANTE B R IY0 G AA1 N T IY0 BRIGANTI B R IH0 G AE1 N T IY0 +BRIGANTINE B R IH2 G AH0 N T IY1 N BRIGGS B R IH1 G Z BRIGGSTONE B R IH1 G S T OW0 N BRIGHAM B R IH1 G AH0 M @@ -15762,10 +15910,12 @@ BRYNGELSON B R IH1 NG G IH0 L S AH0 N BRYON B R AY1 AH0 N BRYS B R IH1 S BRYSON B R AY1 S AH0 N +BRZESKA B R EH1 Z K AA2 BRZEZINSKI B R IH0 Z IH1 N S K IY0 BRZOSKA B R OW1 S K AH0 BRZOZOWSKI B R AH0 Z AO1 F S K IY0 BRZYCKI B R IH1 T S K IY0 +BS B IY2 EH1 S BT B IY1 T IY1 BTA B IY1 T IY1 EY1 BUA B Y UW1 AH0 @@ -15783,6 +15933,7 @@ BUBBLY B AH1 B L IY0 BUBBLY(1) B AH1 B AH0 L IY0 BUBECK B UW1 B EH0 K BUBEL B UW1 B AH0 L +BUBER B UW1 B ER2 BUBIER B Y UW1 B IY0 ER0 BUBINGA B AH0 B IH1 NG G AH0 BUBKA B AH1 B K AH0 @@ -16832,6 +16983,7 @@ BUTENHOFF B Y UW1 T IH0 N HH AO0 F BUTERA B UW0 T EH1 R AH0 BUTERBAUGH B Y UW1 T ER0 B AW0 BUTH B UW1 TH +BUTHAN B UW2 T AA1 N BUTHELEZI B UW2 T AH0 L EY1 Z IY0 BUTHELEZI'S B UW2 T AH0 L EY1 Z IY0 Z BUTKA B AH1 T K AH0 @@ -17072,6 +17224,8 @@ CABBY K AE1 B IY0 CABDRIVER K AE1 B D R AY2 V ER0 CABDRIVERS K AE1 B D R AY2 V ER0 Z CABE K EY1 B +CABELA K AA2 B EH1 L AH0 +CABELA'S K AA2 B EH1 L AH0 CABELL K AA0 B EH1 L CABELLO K AH0 B EH1 L OW0 CABERNET K AE2 B ER0 N EY1 @@ -17183,6 +17337,7 @@ CADENCES K EY1 D AH0 N S IH0 Z CADENHEAD K EY1 D AH0 N HH EH2 D CADET K AH0 D EH1 T CADETS K AH0 D EH1 T S +CADGE K AE1 JH CADIDDLEHOPPER K AH0 D IH1 D AH0 L HH AO2 P ER0 CADIEUX K AE1 D IY0 OW0 CADILLAC K AE1 D AH0 L AE2 K @@ -17714,6 +17869,7 @@ CAMPY K AE1 M P IY0 CAMRO K AE1 M R OW0 CAMRY K AE1 M R IY0 CAMRYS K AE1 M R IY0 Z +CAMS K AE1 M Z CAMSHAFT K AE1 M SH AE2 F T CAMSHAFTS K AE1 M SH AE2 F T S CAMUS K AE1 M IH0 S @@ -17745,6 +17901,8 @@ CANAM K AE1 N AH0 M CANAN K EY1 N AH0 N CANANDAIGUA K AE2 N AH0 N D EY1 G W AH0 CANANEA K AE2 N AH0 N IY1 AH0 +CANAPE K AA1 N AH0 P EY2 +CANAPES K AA1 N AH0 P EY2 Z CANARD K AH0 N AA1 R D CANARIENSIS K AH0 N EH2 R IY0 EH1 N S AH0 S CANARIES K AH0 N EH1 R IY0 Z @@ -17891,6 +18049,7 @@ CANNISTERS K AE1 N IH0 S T ER0 Z CANNISTRARO K AE2 N IH0 S T R AA1 R OW0 CANNIZZARO K AA0 N IY0 T S AA1 R OW0 CANNIZZO K AA0 N IY1 Z OW0 +CANNOCK K AE1 N AH0 K CANNON K AE1 N AH0 N CANNON'S K AE1 N AH0 N Z CANNONBALL K AE1 N AH0 N B AO2 L @@ -18109,6 +18268,7 @@ CAPPY K AE1 P IY0 CAPPY'S K AE1 P IY0 Z CAPRA K AE1 P R AH0 CAPRARO K AA0 P R AA1 R OW0 +CAPRESE K AA2 P R EY1 S EY0 CAPRI K AE1 P R IY0 CAPRI(1) K AH0 P R IY1 CAPRI'S K AE1 P R IY0 Z @@ -18157,6 +18317,7 @@ CAPTURES K AE1 P CH ER0 Z CAPTURING K AE1 P CH ER0 IH0 NG CAPUA K AE1 P Y UW0 AH0 CAPUANO K AA0 P UW0 AA1 N OW0 +CAPULET K AE1 P UW0 L EH2 T CAPUT K AH0 P UH1 T CAPUTI K AA0 P UW1 T IY0 CAPUTO K AA0 P UW1 T OW0 @@ -18203,6 +18364,7 @@ CARAVEO K AA0 R AA1 V IY0 OW0 CARAWAN K AE1 R AH0 W AE0 N CARAWAY K AE1 R AH0 W EY2 CARAWAY(1) K EH1 R AH0 W EY2 +CARB K AA1 R B CARBAJAL K AA0 R B AA0 Y AE1 L CARBALLO K AA0 R B AA1 L OW0 CARBARY K AA1 R B EH0 R IY0 @@ -18237,6 +18399,7 @@ CARBONS K AA1 R B AH0 N Z CARBORUNDUM K AA2 R B ER0 AH1 N D AH0 M CARBOXYLIC K AA0 R B AO0 K S IH1 L IH0 K CARBOY K AA1 R B OY2 +CARBS K AA1 R B Z CARBURETE K AA1 R B Y ER0 EH2 T CARBURETED K AA1 R B Y ER0 EH2 T IH0 D CARBURETION K AA2 R B Y ER0 IY1 SH AH0 N @@ -18326,6 +18489,7 @@ CAREERS K ER0 IH1 R Z CAREFREE K EH1 R F R IY2 CAREFUL K EH1 R F AH0 L CAREFULLY K EH1 R F AH0 L IY0 +CAREFULNESS K EH1 R F AH0 L N EH2 S CAREGIVER K EH1 R G IH2 V ER0 CAREGIVERS K EH1 R G IH2 V ER0 Z CAREGIVING K EH1 R G IH2 V IH0 NG @@ -18754,6 +18918,7 @@ CARTMILL K AA1 R T M IH2 L CARTNER K AA1 R T N ER0 CARTON K AA1 R T AH0 N CARTONEROS K AA2 R T OW2 N EH1 R OW0 S +CARTONNAGE K AA1 R T AH0 N AH0 JH CARTONS K AA1 R T AH0 N Z CARTOON K AA0 R T UW1 N CARTOONING K AA0 R T UW1 N IH0 NG @@ -19009,6 +19174,7 @@ CASTIGATES K AE1 S T AH0 G EY2 T S CASTIGATING K AE1 S T AH0 G EY2 T IH0 NG CASTIGLIA K AA0 S T IY1 G L IY0 AH0 CASTIGLIONE K AA0 S T IY0 G L IY0 OW1 N IY0 +CASTILE K AE2 S T AY1 L CASTILLA K AA0 S T IH1 L AH0 CASTILLE K AE1 S T IH0 L CASTILLEJA K AA0 S T IY0 L EY1 Y AH0 @@ -19208,6 +19374,7 @@ CATHY K AE1 TH IY0 CATHY'S K AE1 TH IY0 Z CATIJA K AH0 T IY1 JH AH0 CATINO K AA0 T IY1 N OW0 +CATION K AE1 T AY2 AH0 N CATKINS K AE1 T K AH0 N Z CATLEDGE K AE1 T L IH0 JH CATLETT K AE1 T L IH0 T @@ -19756,6 +19923,7 @@ CERNIGLIA CH ER2 N IY1 G L IY0 AA0 CERNUDA S ER0 N UW1 D AH0 CERNUDA'S S ER0 N UW1 D AH0 Z CERNY S ER1 N IY0 +CERO S EH1 R OW2 CERONE CH ER0 OW1 N IY0 CERRA S EH1 R AH0 CERRATO CH ER0 AA1 T OW0 @@ -19768,6 +19936,7 @@ CERRUTI CH ER0 UW1 T IY0 CERRUTI(1) S ER0 UW1 T IY0 CERSKA K ER1 S K AH0 CERSKA(1) S ER1 S K AH0 +CERT S ER1 T CERTAIN S ER1 T AH0 N CERTAINLY S ER1 T AH0 N L IY0 CERTAINTEED S ER1 T AH0 N T IY2 D @@ -19835,6 +20004,7 @@ CEYLON(1) S IY0 L AA1 N CEZANNE S EH2 Z AE1 N CEZANNE'S S EH2 Z AE1 N Z CFO S IY1 EH2 F OW1 +CGI S IY2 G IY2 AY1 CHA CH AA1 CHA-CHAS CH AA1 CH AA2 Z CHABLIS SH AH0 B L IY1 @@ -20002,6 +20172,7 @@ CHAN CH AE1 N CHAN'S CH AE1 N Z CHANA CH AE1 N AH0 CHANCE CH AE1 N S +CHANCED CH AE1 N S T CHANCELLOR CH AE1 N S AH0 L ER0 CHANCELLOR(1) CH AE1 N S L ER0 CHANCELLOR'S CH AE1 N S AH0 L ER0 Z @@ -20056,6 +20227,7 @@ CHANNELS CH AE1 N AH0 L Z CHANNING CH AE1 N IH0 NG CHANNON CH AE1 N AH0 N CHANOS CH AA1 N OW0 S +CHANSON SH AA1 N S AO2 N CHANT CH AE1 N T CHANTAL CH AE1 N T AH0 L CHANTED CH AE1 N T IH0 D @@ -20131,6 +20303,7 @@ CHARBONNET SH AA1 R B AH0 N IH0 T CHARBONNET(1) SH AA1 R B AH0 N EY0 CHARCOAL CH AA1 R K OW2 L CHARCOALS CH AA1 R K OW2 L Z +CHARCUTERIE SH AA1 R K UH0 T R IY2 CHARD CH AA1 R D CHARDONNAY CH AA0 R D AA1 N EY0 CHARDONNAYS CH AA0 R D AA1 N EY0 Z @@ -20194,6 +20367,7 @@ CHARLIE CH AA1 R L IY0 CHARLIE'S CH AA1 R L IY0 Z CHARLIER CH AA1 R L IY0 ER0 CHARLINE SH AA0 R L IY1 N +CHARLIZE SH AA2 R L IY1 Z CHARLOT CH AA1 R L AH0 T CHARLOTTE SH AA1 R L AH0 T CHARLOTTE'S SH AA1 R L AH0 T S @@ -20278,6 +20452,7 @@ CHASTISED CH AE0 S T AY1 Z D CHASTISES CH AE0 S T AY1 Z IH0 Z CHASTISING CH AE0 S T AY1 Z IH0 NG CHASTITY CH AE1 S T AH0 T IY0 +CHASUBLE CH AA1 S UW2 B AH0 L CHAT CH AE1 T CHATAQUA SH AH0 T AA1 K W AH0 CHATEAU SH AE0 T OW1 @@ -20397,6 +20572,7 @@ CHEATS CH IY1 T S CHEATUM CH IY1 T AH0 M CHEATWOOD CH IY1 T W UH2 D CHEBRIKOV CH EH1 B R IH0 K AA2 V +CHEBYSHEV CH EH2 B IH0 SH EH1 V CHECCHI CH EH1 K IY0 CHECHEN CH EH1 CH IH0 N CHECHEN'S CH EH1 CH IH0 N Z @@ -20689,6 +20865,8 @@ CHIANESE K IY0 AA0 N EY1 Z IY0 CHIANG CH AE1 NG CHIANG'S CH AE1 NG Z CHIANTI CH IY0 AE1 N T IY0 +CHIAOSCURIST K IY1 AA0 AO2 S K UW2 R IH0 S T +CHIAOSCURO K IY1 AA0 AO2 S K UW2 R OW0 CHIAPAS CH IY0 AA1 P AH0 S CHIAPAS' CH IY0 AA1 P AH0 Z CHIAPPARONE CH IY0 AE1 P ER0 OW2 N @@ -20758,6 +20936,7 @@ CHIEN CH EH1 N CHIENGMAI CH EH1 NG M AY1 CHIESA K IY1 S AH0 CHIFFON SH IH0 F AA1 N +CHIFFRE SH IY1 F R AH0 CHIGGERS CH IH1 G ER0 Z CHIGNEY CH IH1 G N IY0 CHIHUAHUA CH AH0 W AA1 W AA2 @@ -20963,6 +21142,7 @@ CHLOE K L OW1 IY0 CHLOE'S K L OW1 IY0 Z CHLORATE K L AO1 R EY0 T CHLORDANE K L AO1 R D EY2 N +CHLORIC K L AO1 R IH0 K CHLORIDE K L AO1 R AY0 D CHLORINATE K L AO1 R AH0 N EY2 T CHLORINATED K L AO1 R AH0 N EY2 T AH0 D @@ -21021,6 +21201,7 @@ CHOMA CH OW1 M AH0 CHOMBIONO CH AA0 M B IY0 OW1 N OW0 CHOMP CH AA1 M P CHOMPING CH AA1 M P IH0 NG +CHOMSKY CH AA1 M S K IY2 CHON CH AA1 N CHONG CH AO1 NG CHONGQING CH AO1 NG K IH1 NG @@ -21198,6 +21379,7 @@ CHROMATOGRAPHY K R OW0 M AH0 T AA1 G R AH0 F IY0 CHROME K R OW1 M CHROMINANCE K R OW1 M AH0 N AH0 N S CHROMIUM K R OW1 M IY0 AH0 M +CHROMOSOMAL K R OW1 M AH0 S OW2 M AH0 L CHROMOSOME K R OW1 M AH0 S OW2 M CHROMOSOME(1) K R OW1 M AH0 Z OW2 M CHROMOSOMES K R OW1 M AH0 Z OW2 M Z @@ -21332,6 +21514,7 @@ CIA S IY1 AY1 EY1 CIACCIA CH IY0 AH0 CH IY1 AH0 CIACCIA(1) S IY0 AH0 S IY1 AH0 CIACCIO CH AO1 CH IY0 OW0 +CIALIS S IY2 AA1 L AH0 S CIAMPA CH AO1 M P AH0 CIAMPI CH AO1 M P IY0 CIAN SH IY1 N @@ -21450,6 +21633,7 @@ CINEMAX S IH1 N AH0 M AE0 K S CINEPLEX S IH1 N AH0 P L EH2 K S CINEPLEX'S S IH1 N AH0 P L EH2 K S IH0 Z CINERGY S IH1 N ER0 JH IY0 +CINGULAR S IH2 N G UW0 L ER2 CINI CH IY1 N IY0 CINNABAR S IH1 N AH0 B AA2 R CINNABON S IH1 N AH0 B AO2 N @@ -21463,6 +21647,7 @@ CINQUEMANI CH IY0 N K W EH0 M AA1 N IY0 CINRAM S IH1 N R AE0 M CINTHIE S IH1 N TH IY0 CINTRON S IH1 N T R AH0 N +CIO S IY2 AY2 OW1 CIOCCA CH OW1 K AH0 CIOFFI CH IY0 OW1 F IY0 CIOLEK CH IY0 OW1 L EH0 K @@ -21563,6 +21748,7 @@ CISTERN S IH1 S T ER0 N CISTERNS S IH1 S T ER0 N Z CISZEK CH IH1 SH EH0 K CISZEWSKI CH IH0 SH EH1 F S K IY2 +CIT S IY2 AY2 T IY1 CITADEL S IH1 T AH0 D EH2 L CITADEL'S S IH1 T AH0 D EH2 L Z CITATION S AY0 T EY1 SH AH0 N @@ -21615,6 +21801,7 @@ CITY S IH1 T IY0 CITY'S S IH1 T IY0 Z CITYFED S IH1 T IY0 F EH2 D CITYPLACE S IH1 T IY0 P L EY2 S +CITYSEARCH S IH1 T IY0 S ER2 CH CITYSIDE S IH1 T IY0 S AY2 D CITYTRUST S IH1 T IY0 T R AH2 S T CITYWIDE S IH1 T IY0 W AY2 D @@ -21815,6 +22002,7 @@ CLASSIFIABLE K L AE1 S AH0 F AY2 AH0 B AH0 L CLASSIFICATION K L AE2 S AH0 F AH0 K EY1 SH AH0 N CLASSIFICATIONS K L AE2 S AH0 F AH0 K EY1 SH AH0 N Z CLASSIFIED K L AE1 S AH0 F AY2 D +CLASSIFIEDS K L AE1 S AH0 F AY2 D Z CLASSIFIES K L AE1 S AH0 F AY2 Z CLASSIFY K L AE1 S AH0 F AY2 CLASSIFYING K L AE1 S AH0 F AY2 IH0 NG @@ -22027,6 +22215,7 @@ CLEVELANDERS K L IY1 V L AH0 N D ER0 Z CLEVEN K L IY1 V AH0 N CLEVENGER K L EH1 V IH0 N JH ER0 CLEVER K L EH1 V ER0 +CLEVERER K L EH1 V AH0 R ER2 CLEVERLY K L EH1 V ER0 L IY0 CLEVERNESS K L EH1 V ER0 N AH0 S CLEVETRUST K L IY1 V T R AH1 S T @@ -22156,6 +22345,7 @@ CLITES K L AY1 T S CLITORIS K L AY0 T AO1 R IH0 S CLIVE K L AY1 V CLIVER K L AY1 V ER0 +CLO S IY2 EH2 L OW1 CLOAK K L OW1 K CLOAKED K L OW1 K T CLOAKING K L OW1 K IH0 NG @@ -22274,6 +22464,7 @@ CLOUTHIER K L AW1 TH IY0 ER0 CLOUTHIER(1) K L OW1 TH IY0 ER0 CLOUTHIER(2) K L OW1 DH IY0 ER0 CLOUTIER K L AW1 T IY0 ER0 +CLOVE K L OW1 V CLOVER K L OW1 V ER0 CLOVERLEAF K L OW1 V ER0 L IY2 F CLOVES K L OW1 V Z @@ -22359,7 +22550,9 @@ CLYVE K L AY1 V CMOS S IY1 M OW0 S CMOS(1) S IY1 EH1 M OW1 EH1 S CMU S IY1 EH1 M Y UW1 +CMUDICT S IY2 EH2 M Y UW1 D IH2 K T CMX K AH0 M EH1 K S +CNET S IY1 N EH2 T CNN S IY1 EH1 N EH1 N CNN'S S IY1 EH1 N EH1 N Z CNN.COM S IY1 EH1 N EH1 N D AA1 T K AA1 M @@ -22375,6 +22568,7 @@ CO-WORKER K OW1 W ER1 K ER0 CO-WORKERS K OW1 W ER1 K ER0 Z CO. K OW1 CO.(1) K AH1 P AH0 N IY0 +CO2 S IY2 OW2 T UW1 COACH K OW1 CH COACH'S K OW1 CH IH0 Z COACHED K OW1 CH T @@ -23257,6 +23451,7 @@ COMINGS K AH1 M IH0 NG Z COMINO K AH0 M IY1 N OW0 COMINS K OW1 M IH0 N Z COMINSKY K AH0 M IH1 N S K IY0 +COMINTERN K AO1 M IY0 N T ER2 N COMISKEY K OW1 M IH0 S K IY1 COMITATUS K AO0 M AH0 T EY1 T AH0 S COMITO K OW0 M IY1 T OW0 @@ -23346,6 +23541,8 @@ COMMINGLING K AA0 M IH1 NG G AH0 L IH0 NG COMMINGLING(1) K OW0 M IH1 NG G L IH0 NG COMMINS K AA1 M IH0 N Z COMMISERATE K AH0 M IH1 S ER0 EY2 T +COMMISERATES K AH0 M IH1 S ER0 EY2 T S +COMMISERATING K AH0 M IH1 S ER0 EY2 T IH0 NG COMMISH K AH0 M IH1 SH COMMISION K AH0 M IH1 Z AH0 N COMMISION(1) K AH0 M IH1 SH AH0 N @@ -23628,6 +23825,7 @@ COMPOSITIONAL K AA2 M P AH0 Z IH1 SH AH0 N AH0 L COMPOSITIONS K AA2 M P AH0 Z IH1 SH AH0 N Z COMPOST K AA1 M P OW0 S T COMPOSTING K AA1 M P OW2 S T IH0 NG +COMPOSTS K AA1 M P OW0 S T S COMPOSURE K AH0 M P OW1 ZH ER0 COMPOTE K AA1 M P OW0 T COMPOUND K AA1 M P AW0 N D @@ -23757,6 +23955,7 @@ CONCATENATING K AH0 N K AE1 T AH0 N EY2 T IH0 NG CONCATENATION K AH0 N K AE2 T AH0 N EY1 SH AH0 N CONCAVE K AA0 N K EY1 V CONCAVE(1) K AA1 N K EY0 V +CONCAVITY K AA0 N K AA1 V AH0 T IY2 CONCEAL K AH0 N S IY1 L CONCEALED K AH0 N S IY1 L D CONCEALING K AH0 N S IY1 L IH0 NG @@ -23789,6 +23988,8 @@ CONCEPTS K AA1 N S EH0 P T S CONCEPTS(1) K AA1 N S EH0 P S CONCEPTUAL K AH0 N S EH1 P CH UW0 AH0 L CONCEPTUALIZATION K AH0 N S EH1 P CH W AH0 L IH0 Z EY2 SH AH0 N +CONCEPTUALIZE K AH0 N S EH1 P CH W AH0 L AY2 Z +CONCEPTUALIZES K AH0 N S EH1 P CH W AH0 L AY2 Z IH0 Z CONCEPTUALLY K AH0 N S EH1 P CH UW0 AH0 L IY0 CONCERN K AH0 N S ER1 N CONCERN'S K AH0 N S ER1 N Z @@ -23884,6 +24085,7 @@ CONDENSER K AH0 N D EH1 N S ER0 CONDENSING K AH0 N D EH1 N S IH0 NG CONDER K AA1 N D ER0 CONDESCEND K AA2 N D IH0 S EH1 N D +CONDESCENDED K AA2 N D IH0 S EH1 N D IH0 D CONDESCENDING K AA2 N D IH0 S EH1 N D IH0 NG CONDESCENSION K AA2 N D AH0 S EH1 N SH AH0 N CONDIE K AA1 N D IY0 @@ -23967,6 +24169,7 @@ CONFEDERACY'S K AH0 N F EH1 D ER0 AH0 S IY0 Z CONFEDERACY'S(1) K AH0 N F EH1 D R AH0 S IY0 Z CONFEDERATE K AH0 N F EH1 D ER0 AH0 T CONFEDERATE(1) K AH0 N F EH1 D ER0 EY2 T +CONFEDERATES K AH0 N F EH1 D ER0 AH0 T S CONFEDERATION K AH0 N F EH2 D ER0 EY1 SH AH0 N CONFER K AH0 N F ER1 CONFEREE K AA2 N F ER0 IY1 @@ -24137,6 +24340,7 @@ CONGRESSWOMAN'S K AA1 NG G R AH0 S W UH2 M AH0 N Z CONGRESSWOMEN K AA1 NG G R AH0 S W IH2 M IH0 N CONGROVE K AA1 NG G R AH0 V CONGRUENCE K AO1 N G R UW0 AH0 N S +CONGRUENT K AO1 N G R UW0 EH2 N T CONGRUITY K AH0 N G R UW1 AH0 T IY0 CONIC K AA1 N IH0 K CONIC(1) K OW1 N IH0 K @@ -24172,6 +24376,7 @@ CONJUNCTIONS K AH0 N JH AH1 NG K SH AH0 N Z CONJUNCTIVA K AA2 N JH AH0 NG K T AY1 V AH0 CONJURE K AA1 N JH ER0 CONJURED K AA1 N JH ER0 D +CONJURER K AA1 N JH AH0 R AH2 R CONJURES K AA1 N JH ER0 Z CONJURING K AA1 N JH ER0 IH0 NG CONJUROR K AA1 N JH ER0 ER0 @@ -24465,6 +24670,7 @@ CONSTITUTIONALLY K AA2 N S T AH0 T UW1 SH AH0 N AH0 L IY0 CONSTITUTIONIST K AA2 N S T AH0 T UW1 SH AH0 N IH0 S T CONSTITUTIONISTS K AA2 N S T AH0 T UW1 SH AH0 N IH0 S T S CONSTITUTIONS K AA2 N S T IH0 T UW1 SH AH0 N Z +CONSTITUTIVE K AA2 N S T IH1 T UW0 T IH2 V CONSTRAIN K AH0 N S T R EY1 N CONSTRAINED K AH0 N S T R EY1 N D CONSTRAINING K AH0 N S T R EY1 N IH0 NG @@ -24500,6 +24706,7 @@ CONSUL K AA1 N S AH0 L CONSULAR K AA1 N S AH0 L ER0 CONSULATE K AA1 N S AH0 L AH0 T CONSULATES K AA1 N S AH0 L AH0 T S +CONSULS K AA1 N S AH0 L Z CONSULSHIP K AA1 N S AH0 L SH IH2 P CONSULT K AH0 N S AH1 L T CONSULTANCY K AH0 N S AH1 L T AH0 N S IY0 @@ -24569,6 +24776,7 @@ CONTANT K AA1 N T AH0 N T CONTE K AO1 N T CONTE(1) K AO1 N T EY0 CONTEL K AA1 N T EH2 L +CONTEMN K AH2 N T EH1 M CONTEMPLATE K AA1 N T AH0 M P L EY2 T CONTEMPLATED K AA1 N T AH0 M P L EY2 T IH0 D CONTEMPLATES K AA1 N T AH0 M P L EY2 T S @@ -24697,6 +24905,8 @@ CONTRADICTS K AA2 N T R AH0 D IH1 K T S CONTRAN K AA1 N T R AE2 N CONTRAPTION K AH0 N T R AE1 P SH AH0 N CONTRAPTIONS K AH0 N T R AE1 P SH AH0 N Z +CONTRAPUNCTION K AH2 N T R AE0 P UH1 K CH AH0 N +CONTRAPUNCTUAL K AH2 N T R AE0 P UH1 K CH UW2 AH0 L CONTRARIAN K AA2 N T R EH1 R IY0 AH0 N CONTRARIANS K AH0 N T R EH1 R IY0 AH0 N Z CONTRARINESS K AA1 N T R EH0 R IY0 N AH0 S @@ -24852,6 +25062,7 @@ CONYERS K AA1 N Y ER0 Z COO K UW1 COOCHIE K UW1 CH IY0 COODY K UW1 D IY0 +COOED K UW1 D COOGAN K UW1 G AH0 N COOGLE K UW1 G AH0 L COOGLER K UW1 G AH0 L ER0 @@ -25178,6 +25389,7 @@ CORINNA K AO2 R IH1 N AH0 CORINNE K ER0 IY1 N CORINTH K AO1 R AH0 N TH CORINTHIAN K ER0 IH1 N TH IY0 AH0 N +CORINTHIANS K ER0 IH1 N TH IY0 AH0 N Z CORINTO K AO2 R IH1 N T OW0 CORIO K AO1 R IY0 OW0 CORISA K ER0 IY1 S AH0 @@ -25652,6 +25864,7 @@ COTTRILL K AA1 T R AH0 L COTTY K AA1 T IY0 COTUGNO K OW0 T UW1 G N OW0 COTY K OW1 T IY0 +COTYLEDON K AO1 T AH0 L IY2 D AH0 N COU K UW1 COUCH K AW1 CH COUCHED K AW1 CH T @@ -25950,7 +26163,10 @@ COUVILLION K UW0 V IY0 L Y AO1 N COUVILLON K UW0 V IY0 L AO1 N COUZENS K UW1 Z AH0 N Z COVAL K OW0 V AA1 L +COVALENT K OW0 V AA1 L AH0 N T COVALT K OW1 V AA0 L T +COVARIANCE K OW2 V AA1 R IY2 AH0 N S +COVARIES K OW2 V AA1 R IY2 Z COVARRUBIAS K OW0 V AA0 R UW0 B IY1 AH0 Z COVAS K OW1 V AH0 S COVATTA K OW0 V AA1 T AH0 @@ -26158,6 +26374,7 @@ CRAFTSMEN(1) K R AE1 F S M EH0 N CRAFTSPEOPLE K R AE1 F T S P IY2 P AH0 L CRAFTSPEOPLE(1) K R AE1 F S P IY2 P AH0 L CRAFTY K R AE1 F T IY0 +CRAG K R AE1 G CRAGER K R EY1 JH ER0 CRAGG K R AE1 G CRAGGS K R AE1 G Z @@ -26166,6 +26383,7 @@ CRAGHEAD K R AE1 G HH EH2 D CRAGIN K R AE1 JH IH0 N CRAGLE K R EY1 G AH0 L CRAGO K R AA1 G OW0 +CRAGS K R AE1 G Z CRAGUN K R AE1 G AH0 N CRAIB K R EY1 B CRAIG K R EY1 G @@ -26285,6 +26503,7 @@ CRAWFORD'S K R AO1 F ER0 D Z CRAWFORDSVILLE K R AO1 F ER0 D Z V IH2 L CRAWL K R AO1 L CRAWLED K R AO1 L D +CRAWLER K R AO1 L ER0 CRAWLEY K R AO1 L IY0 CRAWLING K R AO1 L IH0 NG CRAWLS K R AO1 L Z @@ -27040,6 +27259,7 @@ CRYMES K R AY1 M Z CRYOGENIC K R AY1 AH0 JH EH2 N IH0 K CRYOGENICS K R AY1 AH0 JH EH2 N IH0 K S CRYOLITE K R AY1 AH0 L AY2 T +CRYONICS K R AY2 AO1 N IH0 K S CRYPT K R IH1 P T CRYPTIC K R IH1 P T IH0 K CRYPTO K R IH1 P T OW0 @@ -27329,6 +27549,7 @@ CURCI K UH1 R CH IY0 CURCIO K UH1 R CH IY0 OW0 CURCURU K UH0 R K UH1 R UW0 CURD K ER1 D +CURDS K ER1 D Z CURE K Y UH1 R CURED K Y UH1 R D CURES K Y UH1 R Z @@ -27526,6 +27747,8 @@ CUTLASS K AH1 T L AH0 S CUTLER K AH1 T L ER0 CUTLER'S K AH1 T L ER0 Z CUTLERY K AH1 T L ER0 IY0 +CUTLET K AH1 T L AH0 T +CUTLETS K AH1 T L AH0 T S CUTLIP K AH1 T L IH0 P CUTOFF K AH1 T AO2 F CUTOFFS K AH1 T AO2 F S @@ -27559,6 +27782,7 @@ CUVELIER K Y UW1 V L IY0 ER0 CUYAHOGA K AY2 AH0 HH OW1 G AH0 CUYLER K AY1 L ER0 CUZZORT K AH1 Z ER0 T +CV S IY2 V IY1 CWIERTNIA K W IY1 R T N IY0 AH0 CWIK K W IH1 K CWIKLA K W IH1 K L AH0 @@ -27593,6 +27817,7 @@ CYCADS S AY1 K AE0 D Z CYCARE S AY1 K EH2 R CYCLADES S AY0 K L EY1 D IY0 Z CYCLADES(1) S AY1 K L AE2 D Z +CYCLAMEN S AY1 K L AH0 M EH2 N CYCLE S AY1 K AH0 L CYCLED S AY1 K AH0 L D CYCLES S AY1 K AH0 L Z @@ -27644,6 +27869,7 @@ CYNICALLY S IH1 N IH0 K AH0 L IY0 CYNICALLY(1) S IH1 N IH0 K L IY0 CYNICISM S IH1 N IH0 S IH2 Z AH0 M CYNICS S IH1 N IH0 K S +CYNOSURE S AY1 N AO0 S ER2 CYNRIC S IH1 N R IH0 K CYNTH S IH1 N TH CYNTHIA S IH1 N TH IY0 AH0 @@ -27767,6 +27993,7 @@ D'SOUZA D IH0 S UW1 Z AH0 D'SOUZA(1) D IY0 S UW1 Z AH0 D. D IY1 D.'S D IY1 Z +D.C. D IY2 S IY1 D.S D IY1 Z DA D AA1 DA(1) D IY1 EY1 @@ -27907,6 +28134,7 @@ DAIGREPONT D EY1 G R IH0 P AA0 N T DAIHATSU D AY2 HH AE1 T S UW0 DAIICHI D AY2 IY1 CH IY0 DAIKIN D EY1 K IH0 N +DAIKON D AY1 K AO2 N DAIL D EY1 L DAILE D EY1 L DAILEY D EY1 L IY0 @@ -27921,6 +28149,7 @@ DAINES D EY1 N Z DAINI D EY1 N IY0 DAINIPPON D EY2 N IH0 P AA1 N DAINS D EY1 N Z +DAINTILY D EY1 N T AH0 L IY2 DAINTY D EY1 N T IY0 DAIQUIRI D AE1 K ER0 IY0 DAIRIES D EH1 R IY0 Z @@ -28851,12 +29080,15 @@ DEBERRY D IY1 B EH0 R IY0 DEBES D IY1 B Z DEBEVOISE D EH2 B EH0 V W AA1 Z DEBI D EH1 B IY0 +DEBIAN D EH1 B IY2 AH0 N DEBIASE D IH0 B IY0 AA1 S IY0 DEBILITATE D AH0 B IH1 L AH0 T EY2 T DEBILITATED D AH0 B IH1 L AH0 T EY2 T IH0 D DEBILITATING D AH0 B IH1 L AH0 T EY2 T IH0 NG DEBILITY D AH0 B IH1 L AH0 T IY0 DEBIT D EH1 B IH0 T +DEBITED D EH1 B IH0 T IH2 D +DEBITS D EH1 B IH0 T S DEBLANC D IH0 B L AE1 NG K DEBLASIO D IH0 B L AA1 S IY0 OW0 DEBLOCK D EH1 B L AH0 K @@ -29076,6 +29308,8 @@ DECLARES D IH0 K L EH1 R Z DECLARING D IH0 K L EH1 R IH0 NG DECLASSIFIED D IH0 K L AE1 S AH0 F AY2 D DECLASSIFY D IH0 K L AE1 S AH0 F AY2 +DECLENSION D AH0 K L EH1 N SH AH0 N +DECLENSIONS D AH0 K L EH1 N SH AH0 N Z DECLERCK D AH0 K L ER1 K DECLERCK'S D AH0 K L ER1 K S DECLERCQ D AH0 K L ER1 K @@ -30027,6 +30261,7 @@ DEMERITT D EH1 M ER0 IH0 T DEMERS D IY1 M ER0 Z DEMERSE D EH1 M ER0 S DEMERY D IH0 M ER1 IY0 +DEMESNE D AH0 M EY1 N DEMETER D IH0 M IY1 T ER0 DEMETRE D EH0 M IY1 T ER0 DEMETRIA D IH0 M EH1 T R IY0 AH0 @@ -30628,6 +30863,7 @@ DERMA D ER1 M AH0 DERMAGRAPH D ER1 M AH0 G R AE0 F DERMAL D ER1 M AH0 L DERMAN D ER1 M AH0 N +DERMATITIS D ER2 M AH0 T AY1 T IH0 S DERMATOLOGICAL D ER2 M AH0 T AH0 L AA1 JH IH0 K AH0 L DERMATOLOGIST D ER2 M AH0 T AA1 L AH0 JH IH0 S T DERMATOLOGISTS D ER2 M AH0 T AA1 L AH0 JH IH0 S T S @@ -30659,6 +30895,7 @@ DERR D EH1 R DERRICK D EH1 R IH0 K DERRICKSON D EH1 R IH0 K S AH0 N DERRICO D IH0 R IY1 K OW0 +DERRIDA D EH2 R IY2 D AA1 DERRIG D EH1 R IH0 G DERRING D EH1 R IH0 NG DERRINGER D EH1 R AH0 N JH ER0 @@ -30736,6 +30973,9 @@ DESCRIBING D IH0 S K R AY1 B IH0 NG DESCRIPTION D IH0 S K R IH1 P SH AH0 N DESCRIPTIONS D IH0 S K R IH1 P SH AH0 N Z DESCRIPTIVE D IH0 S K R IH1 P T IH0 V +DESCRIPTOR D IH0 S K R IH1 P T ER2 +DESCRIPTORS D IH0 S K R IH1 P T ER2 Z +DESCRY D EH0 S K R AY1 DESECRATE D EH0 Z AH0 K R EY1 T DESECRATE(1) D EH0 S AH0 K R EY1 T DESECRATED D EH0 Z AH0 K R EY1 T IH0 D @@ -31632,6 +31872,7 @@ DIEHM D IY1 M DIEKMAN D IY1 K M AH0 N DIEKMANN D IY1 K M AH0 N DIEL D IY1 L +DIELECTRIC D AY2 AH0 L EH1 K T R IH0 K DIEM D IY1 M DIEMER D IY1 M ER0 DIEMERT D IY1 M ER0 T @@ -33590,6 +33831,7 @@ DOMINICK D AA1 M AH0 N IH0 K DOMINIK D AH0 M IH1 N IH0 K DOMINION D AH0 M IH1 N Y AH0 N DOMINION'S D AH0 M IH1 N Y AH0 N Z +DOMINIONS D AH0 M IH1 N Y AH0 N Z DOMINIQUE D AO0 M IH0 N IY1 K DOMINO D AA1 M AH0 N OW2 DOMINO(1) D AA1 M IH0 N OW2 @@ -34054,6 +34296,8 @@ DOUWE D UW1 DOV D AA1 V DOVE D AH1 V DOVE(1) D OW1 V +DOVECOTE D AH1 V K OW2 T +DOVECOTES D AH1 V K OW2 T S DOVEL D OW0 V EH1 L DOVER D OW1 V ER0 DOVER'S D OW1 V ER0 Z @@ -34175,6 +34419,7 @@ DOXY D AA1 K S IY0 DOYAL D OY0 AA1 L DOYEL D OY1 AH0 L DOYEN D OY1 IH0 N +DOYENNE D OY2 EH1 N DOYLE D OY1 L DOYLE'S D OY1 L Z DOYON D OY1 AH0 N @@ -34544,6 +34789,7 @@ DRONET D R OW1 N IH0 T DRONEY D R OW1 N IY0 DRONING D R OW1 N IH0 NG DROOL D R UW1 L +DROOLED D R UW1 L D DROOLING D R UW1 L IH0 NG DROOP D R UW1 P DROOPED D R UW1 P T @@ -34725,6 +34971,7 @@ DUBIN D UW1 B IH0 N DUBININ D UW0 B IH1 N IH0 N DUBINSKY D AH0 B IH1 N S K IY0 DUBIOUS D UW1 B IY0 AH0 S +DUBIOUSLY D UW1 B IY0 AH0 S L IY2 DUBIS D UW1 B IH0 S DUBLIN D AH1 B L IH0 N DUBLIN'S D AH1 B L IH0 N Z @@ -35404,6 +35651,7 @@ DYNAFAC D AY1 N AH0 F AE2 K DYNALECTRIC D AY1 N AH0 L EH2 K T R IH0 K DYNALECTRON D AY1 N AH0 L EH2 K T R AH0 N DYNAMIC D AY0 N AE1 M IH0 K +DYNAMICALLY D AY0 N AE1 M IH0 K L IY2 DYNAMICS D AY0 N AE1 M IH0 K S DYNAMICS' D IH0 N AE1 M IH0 K S DYNAMICS'(1) D AY0 N AE1 M IH0 K S @@ -35464,6 +35712,7 @@ E-MAILING IY1 M EY2 L IH0 NG E-MAILS IY1 M EY2 L Z E. IY1 E.'S IY1 Z +E.G. IY2 G IY1 E.S IY1 Z EACH IY1 CH EACHAN IY1 CH AH0 N @@ -36130,6 +36379,7 @@ EDUCATIONALLY(2) EH2 JH Y UW0 K EY1 SH AH0 N AH0 L IY0 EDUCATIONALLY(3) EH2 JH Y UW0 K EY1 SH N AH0 L IY0 EDUCATIONS EH2 JH AH0 K EY1 SH AH0 N Z EDUCATIONS(1) EH2 JH Y UW0 K EY1 SH AH0 N Z +EDUCATIVE EH2 JH IH0 K EY1 T IH0 V EDUCATOR EH1 JH AH0 K EY2 T ER0 EDUCATOR(1) EH1 JH Y UW0 K EY2 T ER0 EDUCATORS EH1 JH AH0 K EY2 T ER0 Z @@ -36182,6 +36432,8 @@ EFFECTIVELY IH0 F EH1 K T IH0 V L IY0 EFFECTIVELY(1) IY1 F EH0 K T IH0 V L IY0 EFFECTIVENESS IH0 F EH1 K T IH0 V N AH0 S EFFECTIVENESS(1) IY1 F EH0 K T IH0 V N AH0 S +EFFECTOR IH0 F EH1 K T ER0 +EFFECTORS IH0 F EH1 K T ER0 Z EFFECTS IH0 F EH1 K T S EFFECTS(1) IH0 F EH1 K S EFFECTS(2) IY1 F EH0 K T S @@ -36633,6 +36885,7 @@ ELECTIONEERS IH0 L EH2 K SH AH0 N IH1 R Z ELECTIONS IH0 L EH1 K SH AH0 N Z ELECTIVE IH0 L EH1 K T IH0 V ELECTIVES IH0 L EH1 K T IH0 V Z +ELECTOR IH0 L EH1 K T ER0 ELECTORAL IH0 L EH1 K T ER0 AH0 L ELECTORATE IH0 L EH1 K T ER0 AH0 T ELECTORATE(1) IH0 L EH1 K T R IH0 T @@ -36676,8 +36929,10 @@ ELECTRODYNAMIC IH2 L EH2 K T R OW0 D AY2 N AE1 M IH0 K ELECTRODYNAMICS IH2 L EH2 K T R OW0 D AY2 N AE1 M IH0 K S ELECTROLUX EH2 L EH1 K T R AH0 L AH0 K S ELECTROLYSIS EH2 L EH2 K T R AA1 L AH0 S IH0 S -ELECTROLYTIC IH2 L EH2 K T R AH0 L IH1 T IH0 K -ELECTROMAGNET IH2 L EH2 K T R OW0 M AE1 G N AH0 T +ELECTROLYTE EH2 L EH2 K T R AA0 L AY1 T +ELECTROLYTES EH2 L EH2 K T R AA0 L AY1 T S +ELECTROLYTIC EH2 L EH2 K T R AH0 L IH1 T IH0 K +ELECTROMAGNET EH2 L EH2 K T R OW0 M AE1 G N AH0 T ELECTROMAGNETIC IH2 L EH2 K T R OW0 M AE0 G N EH1 T IH0 K ELECTROMAGNETISM IH2 L EH2 K T R OW0 M AE1 G N AH0 T IH2 Z AH0 M ELECTROMAGNETS IH2 L EH2 K T R OW0 M AE1 G N AH0 T S @@ -36700,6 +36955,7 @@ ELECTROSPRAY IH2 L EH1 K T R OW0 S P R EY2 ELECTROSTATIC IH2 L EH2 K T R OW0 S T AE1 T IH0 K ELECTS IH2 L EH1 K T S ELEDGE EH1 L IH0 JH +ELEEMOSYNARY EH2 L AH0 M AO1 S AH0 N EH2 R IY0 ELEEN EH1 L IY2 N ELEFANTE EH0 L EH0 F AA1 N T EH2 ELEGANCE EH1 L AH0 G AH0 N S @@ -36969,6 +37225,7 @@ ELONGATE IH0 L AO1 NG G EY0 T ELONGATED IH0 L AO1 NG G EY0 T AH0 D ELONGATION IY2 L AO0 NG G EY1 SH AH0 N ELOPE IH0 L OW1 P +ELOPED IH0 L OW1 P D ELOPES IH0 L OW1 P S ELOQUENCE EH1 L AH0 K W AH0 N S ELOQUENT EH1 L AH0 K W AH0 N T @@ -37069,6 +37326,7 @@ EMACIATE IH0 M EY1 SH IY0 EY2 T EMACIATED IH0 M EY1 SH IY0 EY2 T IH0 D EMACIATES IH0 M EY1 SH IY0 EY2 T S EMACIATING IH0 M EY1 SH IY0 EY2 T IH0 NG +EMACS IY1 M AE2 K S EMAD IY1 M AE0 D EMAIL IY0 M EY1 L EMAILED IY0 M EY1 L D @@ -37123,6 +37381,7 @@ EMBATTLED EH0 M B AE1 T AH0 L D EMBAYMENT EH0 M B EY1 M AH0 N T EMBED IH0 M B EH1 D EMBEDDED EH0 M B EH1 D IH0 D +EMBEDDING EH0 M B EH1 D IH0 NG EMBELLISH IH0 M B EH1 L IH0 SH EMBELLISHED EH0 M B EH1 L IH0 SH T EMBELLISHES EH0 M B EH1 L IH0 SH AH0 Z @@ -37268,6 +37527,7 @@ EMILY EH1 M IH0 L IY0 EMILY'S EH1 M IH0 L IY0 Z EMINA EH0 M IY1 N AH0 EMINASE EH2 M IH0 N AA1 S IY0 +EMINEM EH2 M IH0 N EH1 M EMINENCE EH1 M AH0 N AH0 N S EMINENCES EH1 M AH0 N AH0 N S IH0 Z EMINENT EH1 M AH0 N AH0 N T @@ -37545,6 +37805,8 @@ ENCLOSURES IH0 N K L OW1 ZH ER0 Z ENCODE EH0 N K OW1 D ENCODED EH0 N K OW1 D IH0 D ENCODING EH0 N K OW1 D IH0 NG +ENCOMIUM EH0 N K AO1 M IH2 AH0 M +ENCOMIUMS EH0 N K AO1 M IH2 AH0 M Z ENCOMPASS EH0 N K AH1 M P AH0 S ENCOMPASSED EH0 N K AH1 M P AH0 S T ENCOMPASSES EH0 N K AH1 M P AH0 S AH0 Z @@ -38259,12 +38521,14 @@ EOS IY1 AO2 S EOS'S IY1 AO2 S IH0 Z EOSINOPHILIA IY2 AO0 S IH2 N AH0 F IH1 L Y AH0 EOSINOPHILIC IY2 AO0 S IH2 N AH0 F IH1 L IH0 K +EPA IY2 P IY2 EY1 EPCOT EH1 P K AO2 T EPEDA EH0 P EY1 D AH0 EPEDA'S EH0 P EY1 D AH0 Z EPES IY1 P S EPHEDRINE IH0 F EH1 D R IH0 N EPHEMERAL IH0 F EH1 M ER0 AH0 L +EPHESUS EH1 F UH0 S AH0 S EPHLIN EH1 F L IH0 N EPHRAIM IY1 F R AH0 M EPHRON EH1 F R AH0 N @@ -38272,9 +38536,12 @@ EPIC EH1 P IH0 K EPIC'S EH1 P IH0 K S EPICENTER EH1 P AH0 S EH2 N T ER0 EPICS EH1 P IH0 K S +EPICTETUS EH2 P IH0 K T IY1 T AH0 S +EPICTETUS(1) EH2 P IH0 K T IY1 SH AH0 S EPICURE EH1 P IH0 K Y UH2 R EPICUREAN EH2 P AH0 K Y UH0 R IY1 AH0 N EPICUREAN(1) EH2 P AH0 K Y UH1 R IY0 AH0 N +EPICURES EH1 P IH0 K Y UH2 R Z EPIDEMIC EH2 P AH0 D EH1 M IH0 K EPIDEMIC(1) EH2 P IH0 D EH1 M IH0 K EPIDEMICS EH2 P AH0 D EH1 M IH0 K S @@ -38295,6 +38562,7 @@ EPILEPSY EH1 P IH0 L EH2 P S IY0 EPILEPTIC EH2 P IH0 L EH1 P T IH0 K EPILEPTICS EH2 P IH0 L EH1 P T IH0 K S EPILOGUE EH2 P IH0 L AO1 G +EPINEPHRINE EH2 P IH0 N EH1 F R IH0 N EPIPHANY IH0 P IH1 F AH0 N IY0 EPISCOPAL IH0 P IH1 S K AH0 P AH0 L EPISCOPALIAN IH0 P IH2 S K AH0 P EY1 L Y AH0 N @@ -38305,12 +38573,15 @@ EPISODES EH1 P IH0 S OW2 D Z EPISODIC EH2 P AH0 S AA1 D IH0 K EPISTEME EH1 P IH0 S T IY2 M EPISTEMIC EH2 P IH0 S T EH1 M IH0 K +EPISTEMOLOGICAL EH0 P IH2 S T AH0 M AA0 L AA1 JH IY2 K AH0 L +EPISTEMOLOGIES EH0 P IH2 S T AH0 M AA1 L AH0 JH IY2 Z EPISTEMOLOGY EH0 P IH2 S T AH0 M AA1 L AH0 JH IY2 EPISTLE IH0 P IH1 S AH0 L EPISTOLARY IH0 P IH1 S T AH0 L EH2 R IY0 EPITAPH EH1 P AH0 T AE2 F EPITAPHS EH1 P AH0 T AE2 F S -EPITHELIAL EH0 P IH0 TH EH1 L Y AH0 L +EPITHELIAL EH2 P IH0 TH IY1 L Y AH0 L +EPITHELIUM EH2 P IH0 TH IY1 L Y AH0 M EPITHET EH1 P AH0 TH EH2 T EPITHETS EH1 P AH0 TH EH2 T S EPITOME IH0 P IH1 T AH0 M IY0 @@ -38328,6 +38599,8 @@ EPOCHAL EH1 P AH0 K AH0 L EPOCHS EH1 P AH0 K S EPOCHS(1) IY1 P AH0 K S EPOGEN EH1 P AH0 JH EH0 N +EPONYMOUS EH0 P AO1 N IH2 M AH0 S +EPONYMY EH0 P AO1 N IH0 M IY2 EPOXY IH0 P AA1 K S IY0 EPP EH1 P EPPARD EH1 P ER0 D @@ -38538,6 +38811,7 @@ ERLANDSON ER1 L AH0 N D S AH0 N ERLANGEN ER0 L AE1 NG G AH0 N ERLANGER EH1 R L AE0 NG ER0 ERLANGER(1) EH1 R L AE0 NG G ER0 +ERLBAUM EH2 R L B AW1 M ERLE EH1 R L AH0 ERLENE ER1 L IY0 N ERLER ER1 L ER0 @@ -38771,6 +39045,7 @@ ESME EH1 Z M ESMERELDA EH0 S M ER0 EH1 L D AH0 ESMINE EH1 Z M AH0 N ESMOND EH1 Z M AH0 N D +ESOPHAGEAL IH0 S AA2 F AH0 G IY1 AH0 L ESOPHAGUS IH0 S AA1 F AH0 G AH0 S ESOTERIC EH2 S AH0 T EH1 R IH0 K ESOTERIC(1) EH2 S OW0 T EH1 R IH0 K @@ -38955,6 +39230,7 @@ ESZTERHAS EH1 S T ER0 HH AA0 S ET EH1 T ETABLISSEMENTS EH2 T AE0 B L IH2 S AH0 M AA1 N T S ETC EH2 T S EH1 T ER0 AH0 +ETC. EH2 T S EH1 T ER0 AH0 ETCETERA EH1 T S EH1 T ER0 AH0 ETCH EH1 CH ETCHED EH1 CH T @@ -39628,6 +39904,8 @@ EXCORIATING EH0 K S K AO1 R IY0 EY2 T IH0 NG EXCORIATION EH0 K S K AO1 R IY0 EY2 SH AH0 N EXCREMENT EH1 K S K R AH0 M AH0 N T EXCRETE IH0 K S K R IY1 T +EXCRETED IH0 K S K R IY1 T IH0 D +EXCRETES IH0 K S K R IY1 T Z EXCRETION IH0 K S K R IY1 SH AH0 N EXCRETORY EH1 K S K R AH0 T AO2 R IY0 EXCRUCIATING IH0 K S K R UW1 SH IY0 EY2 T IH0 NG @@ -40290,6 +40568,7 @@ FACTIONS F AE1 K SH AH0 N Z FACTITIOUS F AE0 K T IH1 SH AH0 S FACTLY F AE1 K T L IY0 FACTO F AE1 K T OW0 +FACTOID F AE1 K T OY2 D FACTOR F AE1 K T ER0 FACTORED F AE1 K T ER0 D FACTORIES F AE1 K T ER0 IY0 Z @@ -41062,6 +41341,9 @@ FAZZINO F AA0 T S IY1 N OW0 FAZZIO F AE1 Z IY0 OW0 FBI EH1 F B IY1 AY1 FBI'S EH1 F B IY1 AY1 Z +FCC EH2 F S IY2 S IY1 +FCC'S EH2 F S IY2 S IY1 Z +FDA EH2 F D IY2 EY1 FE F EY1 FE'S F EY1 Z FEAGAN F EY1 G AH0 N @@ -41702,6 +41984,7 @@ FESTOONED F EH2 S T UW1 N D FESTS F EH1 S T S FESTSPIELHAUS F EH1 S T S P IY1 L HH AW2 S FESTUS F EH1 S T AH0 S +FETA F EH1 T AA2 FETAL F IY1 T AH0 L FETCH F EH1 CH FETCHED F EH1 CH T @@ -41786,6 +42069,7 @@ FIAT F AY1 AE0 T FIAT'S F IY1 AE2 T S FIATO F IY0 AE1 T OW0 FIATO(1) F Y AE1 T OW0 +FIB F IH1 B FIBER F AY1 B ER0 FIBER'S F AY1 B ER0 Z FIBERBOARD F AY1 B ER0 B AO2 R D @@ -41875,6 +42159,7 @@ FIDUCIARES F IH0 D UW1 S IY0 EH2 R Z FIDUCIARES(1) F IH0 D UW1 S IY0 EH2 R IY0 Z FIDUCIARIES F IH0 D UW1 SH IY0 EH2 R IY0 Z FIDUCIARY F AH0 D UW1 SH IY0 EH2 R IY0 +FIE F IY1 FIEBELKORN F IY1 B IH0 L K ER0 N FIEBER F IY1 B ER0 FIEBIG F IY1 B IH0 G @@ -42371,6 +42656,7 @@ FIREFIGHTING F AY1 R F AY2 T IH0 NG FIREFIGHTS F AY1 R F AY2 T S FIREFLIES F AY1 ER0 F L AY2 Z FIREFLY F AY1 ER0 F L AY2 +FIREFOX F AY1 ER0 F AO2 K S FIREHOUSE F AY1 ER0 HH AW2 S FIREHOUSES F AY1 ER0 HH AW2 S IH0 Z FIREMAN F AY1 R M AH0 N @@ -43228,6 +43514,7 @@ FLUTTERS F L AH1 T ER0 Z FLUTY F L UW1 T IY0 FLUVIAL F L UW1 V IY0 AH0 L FLUX F L AH1 K S +FLUXES F L AH1 K S IH0 Z FLUXIONAL F L AH1 K SH AH0 N AH0 L FLY F L AY1 FLYBY F L AY1 B AY2 @@ -43244,6 +43531,8 @@ FLYTRAP F L AY1 T R AE2 P FLYWAY F L AY1 W EY2 FLYWHEEL F L AY1 W IY2 L FM EH1 F EH1 M +FNMA EH2 F EH2 N EH2 M EY1 +FNMA(1) F AE2 N IY2 M EY1 FOAL F OW1 L FOALE F OW1 L FOALE'S F OW1 L Z @@ -43353,6 +43642,7 @@ FOLIATE F OW1 L IY0 EY2 T FOLIATION F OW2 L IY0 EY1 SH AH0 N FOLIC F AA1 L IH0 K FOLINO F OW0 L IY1 N OW0 +FOLIO F OW1 L IY2 OW0 FOLK F OW1 K FOLKER F OW1 K ER0 FOLKERS F OW1 K ER0 Z @@ -43488,6 +43778,7 @@ FOOLS F UW1 L Z FOONG F UW1 NG FOOR F UH1 R FOOS F UW1 Z +FOOSBALL F UW1 S B AO2 L FOOSE F UW1 S FOOSHEE F UW1 SH IY0 FOOT F UH1 T @@ -43802,6 +44093,7 @@ FORMATIONS F AO0 R M EY1 SH AH0 N Z FORMATIVE F AO1 R M AH0 T IH0 V FORMATO F AO0 R M AA1 T OW0 FORMATS F AO1 R M AE2 T S +FORMATTING F AO1 R M AE2 T IH0 NG FORMBEY F AO1 R M B IY0 FORMBY F AO1 R M B IY0 FORMED F AO1 R M D @@ -43820,6 +44112,7 @@ FORMOSA F AO0 R M OW1 S AH0 FORMOSO F AO0 R M OW1 S OW0 FORMS F AO1 R M Z FORMULA F AO1 R M Y AH0 L AH0 +FORMULAE F AO1 R M Y AH0 L EY2 FORMULAIC F AO2 R M Y AH0 L EY1 IH0 K FORMULARY F AO1 R M Y AH0 L EH2 R IY0 FORMULAS F AO1 R M Y AH0 L AH0 Z @@ -44842,6 +45135,8 @@ FRIVOLOUSLY F R IH1 V AH0 L AH0 S L IY0 FRIX F R IH1 K S FRIZELL F R IH1 Z AH0 L FRIZZELL F R IH1 Z AH0 L +FRIZZLE F R IH1 Z AH0 L +FRIZZLED F R IH1 Z AH0 L D FRO F R OW1 FROBERG F R OW1 B ER0 G FROCK F R AA1 K @@ -44993,6 +45288,7 @@ FRYREAR F R AY1 R IH2 R FRYSINGER F R IH1 S IH0 N JH ER0 FSI F S IY1 FTHENAKIS F TH EH0 N AA1 K IH0 S +FTP EH2 F T IY2 P IY1 FU F UW1 FUA F UW1 AH0 FUCCI F UW1 CH IY0 @@ -45007,6 +45303,8 @@ FUCKERS F AH1 K ER0 Z FUCKING F AH1 K IH0 NG FUCKS F AH1 K S FUDALA F UW0 D AA1 L AH0 +FUDDLE F UH1 D AH0 L +FUDDLES F UH1 D AH0 L Z FUDDRUCKER F AH1 D R AH0 K ER0 FUDDRUCKERS F AH1 D R AH0 K ER0 Z FUDDY F AH1 D IY0 @@ -45091,6 +45389,7 @@ FULCRUM F UH1 L K R AH0 M FULD F UH1 L D FULENWIDER F Y UW1 L IH0 N W AY0 D ER0 FULFER F UH1 L F ER0 +FULFIL F UH0 L F IH1 L FULFILL F UH0 L F IH1 L FULFILLED F UH0 L F IH1 L D FULFILLING F UH0 L F IH1 L IH0 NG @@ -45363,8 +45662,10 @@ FUSSNER F AH1 S N ER0 FUSSY F AH1 S IY0 FUST F AH1 S T FUSTAT F AH1 S T AE0 T +FUSTIAN F AH1 S T IY2 AH0 N FUSTOK F AH1 S T AA0 K FUSTON F AH1 S T AH0 N +FUSTY F AH1 S T IY2 FUTCH F AH1 CH FUTHER F AH1 DH ER0 FUTILE F Y UW1 T AH0 L @@ -45893,6 +46194,7 @@ GANGI G AE1 N JH IY0 GANGING G AE1 NG IH0 NG GANGL G AE1 NG G AH0 L GANGLIA G AE1 NG G L IY0 AH0 +GANGLION G AE1 NG G L IY0 AA0 N GANGLIONIC G AE2 NG G L IY0 AA1 N IH0 K GANGLOFF G AE1 NG G L AO0 F GANGLY G AE1 NG L IY0 @@ -45935,6 +46237,7 @@ GANYMEDE G AE1 N AH0 M IY2 D GANZ G AE1 N Z GANZEL G AE1 N Z AH0 L GANZER G AE1 N Z ER0 +GAO G AW1 GAONA G AA0 OW1 N AH0 GAP G AE1 P GAP'S G AE1 P S @@ -46362,6 +46665,7 @@ GAUCHO G AW1 CH OW0 GAUCHOS G AW1 CH OW0 Z GAUDET G OW0 D EH1 T GAUDETTE G OW0 D EH1 T +GAUDIER G AO2 D IY0 EY1 GAUDIN G OW0 D AE1 N GAUDINO G AO2 D IY1 N OW0 GAUDIO G AO1 D IY0 OW0 @@ -46503,6 +46807,7 @@ GAZONSKY'S G AH0 Z AA1 N S K IY0 Z GAZPROM G AE1 Z P R AA2 M GAZZOLA G AA0 T S OW1 L AH0 GDANSK G D AE1 N S K +GDP JH IY2 D IY2 P IY1 GEAC G IY1 K GEAC(1) JH IY1 IY1 EY1 S IY1 GEAGEA JH IY1 AH0 JH IY1 AH0 @@ -46711,6 +47016,7 @@ GENCORP'S JH EH1 N K AO2 R P S GENCORP'S(1) JH EH1 N K AO2 R S GENDARME ZH AA1 N D AA2 R M GENDER JH EH1 N D ER0 +GENDERED JH EH1 N D ER0 D GENDERS JH EH1 N D ER0 Z GENDLER JH EH1 N D L ER0 GENDREAU ZH IH0 N D R OW1 @@ -46719,6 +47025,7 @@ GENDRISEK'S JH EH1 D R IH0 S EH2 K S GENDRON JH EH1 N D R AH0 N GENE JH IY1 N GENE'S JH IY1 N Z +GENEALOGICAL JH IY2 N IY0 AA0 L AO1 JH IH0 K AH0 L GENEALOGY JH IY2 N IY0 AA1 L AH0 JH IY0 GENEEN JH AH0 N IY1 N GENEGO G EH1 N AH0 G OW2 @@ -46915,6 +47222,7 @@ GEOCENTRIC JH IY2 OW0 S EH1 N T R IH0 K GEOCHEMISTRY JH IY2 OW0 K EH1 M AH0 S T R IY0 GEODESIC JH IY2 AH0 D EH1 S IH0 K GEODESY JH IY0 AA1 D AH0 S IY0 +GEODETIC JH IY2 AH0 D EH1 T IH0 K GEODYNE JH IY1 OW0 D AY2 N GEOFF JH EH1 F GEOFFREY JH EH1 F R IY0 @@ -47019,6 +47327,8 @@ GERBER'S G ER1 B ER0 Z GERBERDING G ER1 B ER0 D IH0 NG GERBERT G ER1 B ER0 T GERBIG G ER1 B IH0 G +GERBIL JH ER1 B IH0 L +GERBILS JH ER1 B IH0 L S GERBINO JH ER0 B IY1 N OW0 GERBRANDT G ER1 B R AE2 N T GERCHAS G ER1 CH AH0 Z @@ -47153,7 +47463,10 @@ GERTRUDE G ER1 T R UW0 D GERTSCH G ER1 CH GERTY JH ER1 T IY0 GERTZ G ER1 T S +GERUND JH EH1 R AH0 N D +GERUNDS JH EH1 R AH0 N D Z GERVAIS ZH ER0 V EY1 +GERVAIS(1) ZH ER0 V EY1 Z GERVASE G ER1 V AH0 S GERVASI JH ER0 V AA1 S IY0 GERVASIO JH ER0 V AA1 S IY0 OW0 @@ -47403,6 +47716,7 @@ GIDCUMB G IH1 D K AH0 M GIDDENS G IH1 D AH0 N Z GIDDINGS G IH1 D IH0 NG Z GIDDY G IH1 D IY0 +GIDE ZH IY1 D GIDEL G AY1 D EH2 L GIDEON G IH1 D IY0 AH0 N GIDGET G IH1 JH AH0 T @@ -47786,12 +48100,14 @@ GISSENDANNER G IH1 S IH0 N D AH0 N ER0 GISSI G IH1 S IY0 GIST JH IH1 S T GISU JH IH1 S UW0 +GIT G IH1 T GITANA JH IY0 T AE1 N AH0 GITANO G IH0 T AA1 N OW0 GITANO'S G IH0 T AA1 N OW0 Z GITCHELL JH IH1 CH AH0 L GITHA JH IH1 DH AH0 GITHENS G IH1 TH AH0 N Z +GITHUB G IH1 T HH AH0 B GITLIN JH IH1 T L IH0 N GITTELMAN G IH1 T AH0 L M AH0 N GITTENS G IH1 T AH0 N Z @@ -47897,6 +48213,7 @@ GLADYS G L AE1 D IH0 S GLADYS' G L AE1 D IH0 S GLAESER G L EY1 Z ER0 GLAHN G L AE1 N +GLAM G L AE1 M GLAMOR G L AE1 M ER0 GLAMORIZE G L AE1 M ER0 AY2 Z GLAMORIZED G L AE1 M ER0 AY0 Z D @@ -48243,6 +48560,7 @@ GLYNIS G L IH1 N IH0 S GLYNN G L IH1 N GLYNNIE G L IH1 N IY0 GLYNNIS G L IH1 N IH0 S +GM JH IY2 EH1 M GMBH G AH0 M GMBH(1) JH IY1 EH1 M B IY1 EY1 CH GNAGEY N AE1 JH IY0 @@ -48273,6 +48591,7 @@ GNOMES N OW1 M Z GNOMIC N OW1 M IH0 K GNOMONIC N OW0 M AA1 N IH0 K GNOSTICISM N AA1 S T IH0 S IH2 Z AH0 M +GNP JH IY2 EH2 N P IY1 GNU N UW1 GO G OW1 GO-CART G OW1 K AA2 R T @@ -49026,6 +49345,7 @@ GOTTSHALL G AA1 CH AH0 L GOTTWALD G AA1 T W AH0 L D GOTWALT G AA1 T W AH0 L T GOTZ G AA1 T S +GOUACHE G UW1 AA2 SH GOUCHER G AW1 K ER0 GOUDE G AW1 D GOUDEAU G UW2 D OW1 @@ -49555,6 +49875,7 @@ GRAUL G R AO1 L GRAUMAN G R AO1 M AH0 N GRAUMANN G R AO1 M AH0 N GRAUNKE G R AO1 NG K +GRAVAMEN G R AA0 V EY1 M AH0 N GRAVANO G R AH0 V AA1 N OW0 GRAVANO(1) G R AH0 V AE1 N OW0 GRAVATT G R AE1 V AH0 T @@ -50038,6 +50359,7 @@ GRIPES G R AY1 P S GRIPING G R AY1 P IH0 NG GRIPP G R IH1 P GRIPPED G R IH1 P T +GRIPPER G R IH1 P ER2 GRIPPI G R IH1 P IY0 GRIPPING G R IH1 P IH0 NG GRIPPO G R IH1 P OW0 @@ -50171,6 +50493,7 @@ GROOVIEST G R UW1 V IY0 AH0 S T GROOVY G R UW1 V IY0 GROPE G R OW1 P GROPED G R OW1 P T +GROPES G R OW1 P S GROPING G R OW1 P IH0 NG GROPP G R AA1 P GROPPER G R AA1 P ER0 @@ -50323,6 +50646,7 @@ GRUDGINGLY G R AH1 JH IH0 NG L IY0 GRUDGINGLY(1) G R AH1 JH IH0 NG G L IY0 GRUDZIEN G R AH1 D Z IY0 N GRUDZINSKI G R AH0 JH IH1 N S K IY0 +GRUE G R UW1 GRUEL G R UW1 IH0 L GRUELING G R UW1 IH0 L IH0 NG GRUELING(1) G R UW1 L IH0 NG @@ -50358,6 +50682,7 @@ GRUMMOND'S G R AH1 M AH0 N D Z GRUMP G R AH1 M P GRUMPIER G R AH1 M P IY0 ER0 GRUMPIER(1) G R AH1 M P Y ER0 +GRUMPINESS G R AH1 M P IY0 N EH2 S GRUMPY G R AH1 M P IY0 GRUN G R AH1 N GRUNBERG G R AH1 N B ER0 G @@ -51148,6 +51473,7 @@ HABERLAND HH AE1 B ER0 L AH0 N D HABERLE HH AE1 B ER0 AH0 L HABERMAN HH EY1 B ER0 M AH0 N HABERMANN HH EY1 B ER0 M AH0 N +HABERMAS HH AA1 B ER0 M AA2 S HABERMEHL HH AE1 B ER0 M AH0 L HABERSON HH EY1 B ER0 S IH0 N HABERSON(1) HH AE1 B ER0 S IH0 N @@ -51218,6 +51544,7 @@ HACKWORTH HH AE1 K W ER2 TH HAD HH AE1 D HADA HH AA1 D AH0 HADAD HH AE1 D AH0 D +HADAR HH AE1 D ER2 HADAWAY HH AA1 D AH0 W EY0 HADD HH AE1 D HADDAD HH AE1 D AH0 D @@ -51715,6 +52042,7 @@ HAMIL HH AE1 M AH0 L HAMILL HH AE1 M AH0 L HAMILTON HH AE1 M AH0 L T AH0 N HAMILTON'S HH AE1 M AH0 L T AH0 N Z +HAMILTONIAN HH AE1 M AH0 L T OW2 N Y AH0 N HAMILTONS HH AE1 M AH0 L T AH0 N Z HAMISH HH AE1 M IH0 SH HAMITER HH AE1 M AY0 T ER0 @@ -51776,6 +52104,7 @@ HAMMOND HH AE1 M AH0 N D HAMMONDS HH AE1 M AH0 N D Z HAMMONS HH AE1 M AH0 N Z HAMMONTREE HH AE0 M AH0 N T R IY1 +HAMMURABI HH AE1 M AH0 R AA2 B IY2 HAMNER HH AE1 M N ER0 HAMON HH AE1 M AH0 N HAMOR HH AE1 M ER0 @@ -52826,6 +53155,7 @@ HAUGLAND HH AO1 G L AH0 N D HAUK HH AO1 K HAUKE HH AO1 K HAUL HH AO1 L +HAULAGE HH AO1 L AH0 JH HAULED HH AO1 L D HAULER HH AO1 L ER0 HAULERS HH AO1 L ER0 Z @@ -53383,6 +53713,7 @@ HEEDING HH IY1 D IH0 NG HEEDS HH IY1 D Z HEEFNER HH IY1 F N ER0 HEEG HH IY1 G +HEEHAW HH IY1 HH AW2 HEEKE HH IY1 K HEEKIN HH IY1 K IH0 N HEEL HH IY1 L @@ -53467,6 +53798,7 @@ HEIDBREDER HH AY1 D B R IH0 D ER0 HEIDBRINK HH AY1 D B R IH0 NG K HEIDE HH AY1 D HEIDECKER HH AY1 D IH0 K ER0 +HEIDEGGER HH AY1 D IH0 G ER0 HEIDEL HH AY1 D AH0 L HEIDELBERG HH AY1 D AH0 L B ER0 G HEIDELBERG'S HH AY1 D AH0 L B ER0 G Z @@ -55117,6 +55449,7 @@ HITTNER HH IH1 T N ER0 HITTY HH IH1 T IY0 HITZ HH IH1 T S HITZEMAN HH IH1 T S M AH0 N +HIV EY2 CH AY2 V IY1 HIVE HH AY1 V HIVELY HH AY1 V L IY0 HIVES HH AY1 V Z @@ -56609,6 +56942,8 @@ HSIEH SH IY0 EH1 HSIUNG SH IY0 AH1 NG HSIUNG'S SH Y AH1 NG Z HSU SH UW1 +HTML EY2 CH T IY2 EH2 M EH1 L +HTTP EY2 CH T IY2 T IY2 P IY1 HU HH UW1 HUA HH UW1 AH0 HUA(1) HH W AA1 @@ -57166,7 +57501,7 @@ HURRAY HH AH0 R EY1 HURRELL HH AO1 R AH0 L HURRI HH ER1 IY0 HURRICANE HH ER1 AH0 K EY2 N -HURRICANE(1) HH AH1 R AH0 K EY2 N Z +HURRICANE(1) HH AH1 R AH0 K EY2 N HURRICANE'S HH ER1 AH0 K EY2 N Z HURRICANES HH ER1 AH0 K EY2 N Z HURRIED HH ER1 IY0 D @@ -57297,6 +57632,7 @@ HUYETT HH AY1 IH0 T HUYLER HH AY1 L ER0 HUYNH HH AY1 N HUYSER HH AY1 S ER0 +HUZZAH HH UH2 Z AA1 HWA HH W AA1 HWAN HH W AA1 N HWAN'S HH W AA1 N Z @@ -57358,6 +57694,8 @@ HYDRO HH AY1 D R OW2 HYDRO'S HH AY1 D R OW2 Z HYDROCARBON HH AY2 D R OW0 K AA1 R B AH0 N HYDROCARBONS HH AY2 D R OW0 K AA1 R B AH0 N Z +HYDROCEPHALIC HH AY2 D R OW0 S EH0 F AA1 L IH0 K +HYDROCEPHALUS HH AY2 D R OW0 S EH1 F AH0 L AH0 S HYDROELECTRIC HH AY2 D R OW0 IH0 L EH1 K T R IH0 K HYDROENCEPHALUS HH AY2 D R OW0 AH0 N S EH1 F AH0 L AH0 S HYDROFOIL HH AY1 D R AH0 F OY2 L @@ -57415,6 +57753,7 @@ HYMANS HH AY1 M AH0 N Z HYMAS HH AY1 M AH0 Z HYMEL HH AY1 M AH0 L HYMEN HH AY1 M AH0 N +HYMENEAL HH IH2 M AH0 N IY1 AH0 L HYMER HH AY1 M ER0 HYMES HH AY1 M Z HYMIE HH AY1 M IY0 @@ -57502,6 +57841,7 @@ HYPOLITE HH AY1 P AH0 L AY0 T HYPONEX HH AY1 P OW0 N EH2 K S HYPOTENSION HH AY2 P OW0 T EH1 N SH AH0 N HYPOTHALAMIC HH AY2 P OW0 TH AH0 L AE1 M IH0 K +HYPOTHALAMUS HH AY2 P OW0 TH AA1 L AH0 M AH0 S HYPOTHEKEN HH AY2 P AA1 TH AH0 K AH0 N HYPOTHERMIA HH AY2 P AH0 TH ER1 M IY0 AH0 HYPOTHESES HH AY0 P AA1 TH AH0 S IY2 Z @@ -57537,6 +57877,7 @@ HYUNDAI(1) HH AH1 N D EY2 HYUNDAI'S HH AH1 N D EY2 Z HYUNDAIS HH Y AH1 N D EY2 Z HYWELL HH AY1 W EH0 L +HZ HH ER1 T Z I AY1 I'D AY1 D I'ERS AY1 ER0 Z @@ -57598,6 +57939,8 @@ IBERIA'S AY0 B IH1 R IY0 AH0 Z IBERIAN AY0 B IH1 R IY0 AH0 N IBERO IH2 B EH1 R OW0 IBEX AY1 B EH0 K S +IBID IH1 B IH0 D +IBID. IH1 B IH0 D IBIS AY1 B AH0 S IBMER IH1 B M ER0 IBMERS IH1 B M ER0 Z @@ -57757,6 +58100,7 @@ IDS(1) IH1 D Z IDUNA IH2 D UW1 N AH0 IDYLL AY1 D AH0 L IDYLLIC AY0 D IH1 L IH0 K +IEEE AY2 T R IH2 P L AH0 IY1 IERARDI IY0 ER0 AA1 R D IY0 IERNE IH1 R N IEZZI IY0 EH1 T S IY0 @@ -58205,6 +58549,7 @@ IMPLAUSIBLE IH2 M P L AO1 Z AH0 B AH0 L IMPLAUSIBLY IH2 M P L AO1 Z AH0 B L IY0 IMPLEMENT IH1 M P L AH0 M AH0 N T IMPLEMENTATION IH2 M P L AH0 M EH0 N T EY1 SH AH0 N +IMPLEMENTATIONS IH2 M P L AH0 M EH0 N T EY1 SH AH0 N Z IMPLEMENTED IH1 M P L AH0 M EH2 N T AH0 D IMPLEMENTED(1) IH1 M P L AH0 M EH2 N AH0 D IMPLEMENTING IH1 M P L AH0 M EH2 N T IH0 NG @@ -58497,6 +58842,7 @@ INCHES(1) IH1 N CH IH0 Z INCHES' IH1 N CH AH0 Z INCHES'(1) IH1 N CH IH0 Z INCHING IH1 N CH IH0 NG +INCHOATE IH2 N K OW1 AH0 T INCHON IH1 N CH AO0 N INCIDENCE IH1 N S AH0 D AH0 N S INCIDENCE(1) IH1 N S IH0 D AH0 N S @@ -58630,6 +58976,7 @@ INCRUST IH2 N K R AH1 S T INCRUSTATION IH2 N K R AH0 S T EY1 SH AH0 N INCSTAR IH1 NG K S T AA2 R INCUBATE IH1 N K Y AH0 B EY2 T +INCUBATES IH1 N K Y AH0 B EY2 T S INCUBATING IH1 N K Y AH0 B EY2 T IH0 NG INCUBATION IH2 NG K Y UW0 B EY1 SH AH0 N INCUBATOR IH1 NG K Y AH0 B EY2 T ER0 @@ -58661,6 +59008,7 @@ INDECISIVE IH2 N D IH0 S AY1 S IH0 V INDECISIVENESS IH2 N D EH1 S IH0 S IH0 V N AH0 S INDEED IH2 N D IY1 D INDEFATIGABLE IH2 N D IH0 F AE1 T IH0 G AH0 B AH0 L +INDEFEASIBLE IH2 D AH0 F IY1 Z AH0 B AH0 L INDEFENSIBLE IH2 N D IH0 F EH1 N S AH0 B AH0 L INDEFINABLE IH2 N D IH0 F AY1 N AH0 B AH0 L INDEFINITE IH2 N D EH1 F AH0 N AH0 T @@ -58790,6 +59138,7 @@ INDO IH1 N D OW0 INDO-EUROPEAN IH2 N D OW0 Y UH2 R AH0 P IY1 AH0 N INDOCHINA IH2 N D OW0 CH AY1 N AH0 INDOCHINESE IH2 N D OW0 CH AY2 N IY1 Z +INDOCTRINATE IH0 N D AA1 K T R AH0 N EY2 T INDOCTRINATED IH2 N D AA1 K T R AH0 N EY2 T IH0 D INDOCTRINATION IH2 N D AA2 K T R AH0 N EY1 SH AH0 N INDOLENT IH1 N D AH0 L AH0 N T @@ -59592,6 +59941,7 @@ INSUBSTANTIATE(1) IH2 N S AH0 B S T AE1 N SH IY2 EY0 T INSUBSTANTIATED IH2 N S AH0 B S T AE1 N CH IY2 EY0 T AH0 D INSUBSTANTIATED(1) IH2 N S AH0 B S T AE1 N SH IY2 EY0 T AH0 D INSUFFERABLE IH2 N S AH1 F ER0 AH0 B AH0 L +INSUFFICIENCY IH2 N S AH0 F IH1 SH AH0 N S IY2 INSUFFICIENT IH2 N S AH0 F IH1 SH AH0 N T INSUFFICIENTLY IH2 N S AH0 F IH1 SH AH0 N T L IY0 INSULAR IH1 N S AH0 L ER0 @@ -59974,6 +60324,8 @@ INTERPLAY IH1 N T ER0 P L EY2 INTERPOL IH1 N T ER0 P OW2 L INTERPOLATE IH2 T ER1 P AH0 L EY2 T INTERPOLATED IH2 T ER1 P AH0 L EY2 T IH0 D +INTERPOLATION IH2 T ER1 P AH0 L EY2 SH AH0 N +INTERPOLATIONS IH2 T ER1 P AH0 L EY2 SH AH0 N Z INTERPOSE IH2 N T ER0 P OW1 Z INTERPRET IH2 N T ER1 P R AH0 T INTERPRETATION IH2 N T ER2 P R IH0 T EY1 SH AH0 N @@ -60029,6 +60381,7 @@ INTERSTATE IH2 N T ER0 S T EY1 T INTERSTATE'S IH2 N T ER0 S T EY1 T S INTERSTATES IH1 N T ER0 S T EY2 T S INTERSTELLAR IH2 N T ER0 S T EH1 L ER0 +INTERSTITIAL IH2 N T ER0 S T IH1 SH AH0 L INTERTAN IH2 N T ER0 T AE1 N INTERTECH IH1 N T ER0 T EH2 K INTERTECHNOLOGY IH2 N T ER0 T AH0 K N AA1 L AH0 JH IY0 @@ -60108,6 +60461,7 @@ INTOXICATES IH2 N T AA1 K S AH0 K EY2 T S INTOXICATING IH2 N T AA1 K S IH0 K EY2 T IH0 NG INTOXICATION IH2 N T AA2 K S AH0 K EY1 SH AH0 N INTRA IH1 N T R AH0 +INTRACELLULAR IH2 N T R AA0 S EH1 L Y AH0 L ER0 INTRACOMPANY IH2 N T R AH0 K AA1 M P AH0 N IY0 INTRACRANIAL IH2 N T R AH0 K R EY1 N IY0 AH0 L INTRACTABLE IH2 N T R AE1 K T AH0 B AH0 L @@ -60129,6 +60483,7 @@ INTRAVENOUSLY(1) IH2 N T R AH0 V IY1 N AH0 S L IY0 INTRAWEST IH1 N T R AH0 W AH0 S T INTRAWEST(1) IH2 N T R AH0 W EH1 S T INTREPID IH2 N T R EH1 P AH0 D +INTREPIDITY IH2 N T R EH1 P IH0 D AH0 T IY2 INTREPIDLY IH2 N T R EH1 P AH0 D L IY0 INTREX IH1 N T R AH0 K S INTRICACIES IH1 N T R AH0 K AH0 S IY0 Z @@ -60602,6 +60957,7 @@ ISAUTIER AY0 S AO1 T Y ER0 ISAY AY1 S EY2 ISBELL IH1 S B EH0 L ISBILL IH2 S B IH1 L +ISBN AY2 EH2 S B IY2 EH1 N ISCARIOT IH2 S K EH1 R IY0 AH0 T ISCH IH1 SH ISCHEMIA IH2 S K EH1 M IY0 AH0 @@ -61162,6 +61518,7 @@ JAMIE'S JH EY1 M IY0 Z JAMIESON JH EY1 M IH0 S AH0 N JAMILA JH AH0 M IH1 L AH0 JAMISON JH EY1 M IH0 S AH0 N +JAMMAL JH AA0 M AA1 L JAMMED JH AE1 M D JAMMER JH AE1 M ER0 JAMMERS JH AE1 M ER0 Z @@ -61535,6 +61892,8 @@ JEFFY JH EH1 F IY0 JEHLE JH EH1 HH AH0 L JEHOVAH JH AH0 HH OW1 V AH0 JEHOVAH'S JH AH0 HH OW1 V AH0 Z +JEJU JH EH1 JH UW2 +JEJUNE JH EH2 JH UW1 N JEKEL JH EH1 K AH0 L JEKYLL JH EH1 K AH0 L JELEN JH EH1 L AH0 N @@ -61559,6 +61918,7 @@ JEM JH EH1 M JEM'S JH EH1 M Z JEMIE JH EH1 M IY0 JEMIMA JH EH0 M IY1 M AH0 +JEMIMAH JH EH0 M AY1 M AH0 JEMISON JH EH1 M IH0 S AH0 N JEMMIE JH EH1 M IY0 JEMMOTT JH EH1 M AH0 T @@ -61852,6 +62212,7 @@ JINGLES JH IH1 NG G AH0 L Z JINGOISM JH IH1 NG G OW2 IH0 Z AH0 M JINGOISTIC JH IH0 NG G OW0 IH1 S T IH0 K JINGSHENG JH IH1 NG SH EH0 NG +JINK JH IH1 NG K JINKINS JH IH1 NG K IH0 N Z JINKS JH IH1 NG K S JINRIGHT JH IH1 N R AY2 T @@ -61923,6 +62284,7 @@ JOCKEYING JH AA1 K IY0 IH0 NG JOCKEYS JH AA1 K IY0 Z JOCKS JH AA1 K S JOCOSA Y OW0 K OW1 S AH0 +JOCOSE JH AO2 K OW1 Z JOCULAR JH AA1 K Y AH0 L ER0 JOCYLAN JH AO1 S L AH0 N JOCYLAN'S JH AO1 S L AH0 N Z @@ -62245,6 +62607,7 @@ JOYS JH OY1 Z JOYSTICK JH OY1 S T IH2 K JOZEF JH OW1 Z AH0 F JOZWIAK Y AA1 Z V IY0 AE0 K +JR. JH UW1 N ER0 JU JH UW1 JUA JH UW1 AH0 JUAN W AA1 N @@ -62415,6 +62778,7 @@ JUMPY JH AH1 M P IY0 JUN JH AH1 N JUNCO JH AH1 NG K OW0 JUNCTION JH AH1 NG K SH AH0 N +JUNCTIONS JH AH1 NG K SH AH0 N Z JUNCTURE JH AH1 NG K CH ER0 JUNCTURES JH AH1 NG K CH ER0 Z JUNDA JH AH1 N D AH0 @@ -62746,6 +63110,7 @@ KALER K EY1 L ER0 KALETA K AE1 L IH0 T AH0 KALEY K EY1 L IY0 KALGOORLIE K AE2 L G UW1 R L IY0 +KALI K AA1 L IY2 KALIKOW K AE1 L IH0 K OW0 KALIL K AE1 L AH0 L KALIN K AE1 L IH0 N @@ -63053,6 +63418,7 @@ KARIBU K EH2 R IY1 B UW0 KARIM K ER0 IY1 M KARIMI K AA0 R IY1 M IY0 KARIN K EH1 R IH0 N +KARINA K AA0 R IY1 N AH0 KARINO K EH2 R IY1 N OW0 KARIOTIS K AA2 R IY0 OW1 T IH0 S KARIS K EH1 R IY0 Z @@ -63219,6 +63585,7 @@ KASUN K AA1 S UW0 N KASZA K AA1 SH AH0 KASZUBA K AH0 SH UW1 B AH0 KAT K AE1 T +KATANA K AA2 T AA1 N AA2 KATAOKA K AA0 T AA0 OW1 K AH0 KATARINA K AA2 T ER0 IY1 N AH0 KATARINA'S K AA2 T ER0 IY1 N AH0 Z @@ -63830,6 +64197,7 @@ KERCHIEF K ER1 CH AH0 F KERCHIEFS K ER1 CH AH0 F S KERCHNER K ER1 K N ER0 KEREKES K EH1 R IH0 K S +KEREN K EH1 R EH2 N KERESTES K EH1 R IH0 S T S KERESZTES K EH1 R AH0 S T IY0 Z KERFOOT K ER1 F UH0 T @@ -64099,6 +64467,7 @@ KIBBEY K IH1 B IY0 KIBBLE K IH1 B AH0 L KIBBUTZ K IH0 B UH1 T S KIBBUTZIM K IH2 B UH0 T S IH1 M +KIBBUTZNIK K IH0 B UH1 T S N IH0 K KIBBUTZNIKS K IH0 B UH1 T S N IH0 K S KIBBY K IH1 B IY0 KIBEHO K IH1 B AH0 HH OW0 @@ -64229,6 +64598,7 @@ KIKUCHI K IY0 K UW1 CH IY0 KIKUMURA K IY2 K UW2 M UW1 R AH0 KIKWIT K IH1 K W IH0 T KILA K IH1 L AH0 +KILAUEA K IY2 L AW2 EY1 AH0 KILBANE K IH1 L B AH0 N KILBORN K IH1 L B ER0 N KILBORNE K IH1 L B AO2 R N @@ -64386,6 +64756,7 @@ KIN K IH1 N KIN'S K IH1 N Z KINARD K IH1 N ER0 D KINARK K IH1 N AA0 R K +KINASE K AY1 N EY2 Z KINBURN K IH1 N B ER2 N KINCADE K IH2 N K EY1 D KINCAID K IH2 N K EY1 D @@ -66009,6 +66380,7 @@ KRASZEWSKI K R AH0 SH EH1 F S K IY0 KRAT K R AE1 T KRATKY K R AE1 T K IY0 KRATOCHVIL K R AE1 T AH0 K V AH0 L +KRATOS K R AE1 T AO2 S KRATT K R AE1 T KRATZ K R AE1 T S KRATZER K R EY1 T Z ER0 @@ -66590,6 +66962,8 @@ KURLANDER K ER1 L AH0 N D ER0 KURMAN K ER1 M AH0 N KURMEL K ER1 M AH0 L KURNIT K ER1 N IH0 T +KURNOOL K ER2 N UW1 L +KURNUL K ER2 N UH1 L KURODA K ER0 OW1 D AH0 KUROKAWA K UW2 R OW0 K AA1 W AH0 KUROSAWA K UH2 R OW0 S AA1 W AH0 @@ -66854,6 +67228,7 @@ LABYRINTH L AE1 B ER0 IH2 N TH LABYRINTHINE L AE2 B ER0 IH1 N TH IY2 N LAC L AE1 K LAC'S L AE1 K S +LACAN L AA0 K AA1 N LACANA L AA0 K AE1 N AH0 LACASSE L AA0 K AA1 S IY0 LACAVA L AA0 K AA1 V AH0 @@ -66885,6 +67260,7 @@ LACHMAN L AE1 K M AH0 N LACHMAR L AE1 K M AA0 R LACHNEY L AE1 K N IY0 LACHOWICZ L AA1 HH AH0 V IH0 CH +LACHRYMOSE L AE1 K R IY0 M OW2 Z LACINA L AA0 CH IY1 N AH0 LACIVITA L AA0 CH IY0 V IY1 T AH0 LACK L AE1 K @@ -67053,6 +67429,7 @@ LAGGED L AE1 G D LAGGING L AE1 G IH0 NG LAGLE L EY1 G AH0 L LAGNADO L AA2 G N AA1 D OW0 +LAGNIAPPE L AE1 NG AA2 P LAGO L AA1 G OW0 LAGOMARSINO L AA0 G OW2 M AA0 R S IY1 N OW0 LAGOON L AH0 G UW1 N @@ -67949,6 +68326,7 @@ LATENESS L EY1 T N AH0 S LATENT L EY1 T AH0 N T LATER L EY1 T ER0 LATERAL L AE1 T ER0 AH0 L +LATERALLY L AE1 T ER0 AH0 L IY2 LATERITES L AE1 T ER0 AY2 T S LATERRIERE L AA0 T EH2 R Y EH1 R LATERZA L AA0 T EH1 R Z AH0 @@ -69900,6 +70278,7 @@ LIGHTING L AY1 T IH0 NG LIGHTING'S L AY1 T IH0 NG Z LIGHTLE L AY1 T AH0 L LIGHTLY L AY1 T L IY0 +LIGHTMAN L AY1 T M AH2 N LIGHTNER L AY1 T N ER0 LIGHTNESS L AY1 T N AH0 S LIGHTNING L AY1 T N IH0 NG @@ -70040,6 +70419,8 @@ LIMITLESS L IH1 M AH0 T L AH0 S LIMITS L IH1 M AH0 T S LIMITS(1) L IH1 M IH0 T S LIMMER L IH1 M ER0 +LIMN L IH1 M +LIMNED L IH1 M D LIMNOLOGY L IH0 M N AA1 L AH0 JH IY0 LIMO L IH1 M OW0 LIMOGES L IH0 M OW1 JH IH0 Z @@ -70214,6 +70595,7 @@ LINKAGE(1) L IH1 NG K IH0 JH LINKAGES L IH1 NG K IH0 JH IH0 Z LINKE L IH1 NG K LINKED L IH1 NG K T +LINKEDIN L IH1 NG K T IH2 N LINKENHOKER L IH1 NG K IH0 N HH AH0 K ER0 LINKER L IH1 NG K ER0 LINKING L IH1 NG K IH0 NG @@ -70294,6 +70676,7 @@ LIPFORD L IH1 P F ER0 D LIPHAM L IH1 F AH0 M LIPID L AY1 P AH0 D LIPIDE L IH0 P IY1 D +LIPIDS L IH1 P IH0 D Z LIPINSKI L IH0 P IH1 N S K IY0 LIPKA L IH1 P K AH0 LIPKE L IH1 P K @@ -70634,6 +71017,7 @@ LLANES L EY1 N Z LLANO L AA1 N OW0 LLANOS L AA1 N OW0 Z LLANVIEW L AA2 N V Y UW1 +LLC EH2 L EH2 L S IY2 LLERENA L EH0 R EY1 N AH0 LLEWELLYN L UW2 EH1 L IH0 N LLEWELYN L UW1 IH0 L IH0 N @@ -70918,6 +71302,7 @@ LOGICALLY L AA1 JH IH0 K L IY0 LOGICIAN L OW0 JH IH1 SH AH0 N LOGICON L AA1 JH IH0 K AA2 N LOGIE L OW1 JH IY0 +LOGIN L AO1 G IH2 N LOGISTIC L AH0 JH IH1 S T IH0 K LOGISTICAL L AH0 JH IH1 S T IH0 K AH0 L LOGISTICALLY L AH0 JH IH1 S T IH0 K L IY0 @@ -71372,6 +71757,7 @@ LOUANNA L AW0 AA1 N AH0 LOUANNA(1) L UW0 AE1 N AH0 LOUANNE L UW0 AE1 N LOUART L UW1 AA0 R T +LOUCHE L UW1 SH LOUCK L AW1 K LOUCKS L AW1 K S LOUD L AW1 D @@ -71639,6 +72025,8 @@ LUBRICATE L UW1 B R IH0 K EY2 T LUBRICATED L UW1 B R AH0 K EY2 T IH0 D LUBRICATING L UW1 B R AH0 K EY2 T IH0 NG LUBRICATION L UW2 B R IH0 K EY1 SH AH0 N +LUBRICIOUS L UW0 B R IH1 SH AH0 S +LUBRICITY L UW0 B R IH1 S IH0 T IY2 LUBRIZOL L UW1 B R IH0 Z AA0 L LUBY L UW1 B IY0 LUC L UW1 K @@ -72175,6 +72563,7 @@ LYMPHOBLAST L IH2 M F AH0 B L AE1 S T LYMPHOBLASTIC L IH2 M F AH0 B L AE1 S T AH0 K LYMPHOCYTE L IH1 M F AH0 S AY2 T LYMPHOCYTES L IH1 M F AH0 S AY2 T S +LYMPHOID L IH1 M F OY2 D LYMPHOMA L IH0 M F OW1 M AH0 LYMPHOMAS L IH0 M F OW1 M AH0 Z LYN L IH1 N @@ -72262,6 +72651,7 @@ M-CODE EH1 M K OW1 D M-CODES EH1 M K OW1 D Z M. EH1 M M.'S EH1 M Z +M.D. EH2 M D IY1 M.S EH1 M Z M1 EH1 M W AH1 N M2 EH1 M T UW1 @@ -72840,6 +73230,7 @@ MAGRUDER M AH0 G R UW1 D ER0 MAGS M AE1 G Z MAGTEN M AE1 G T EH1 N MAGUIRE M AH0 G W AY1 R +MAGUS M EY1 G AH0 S MAGWOOD M AE1 G W UH2 D MAGYAR M AE1 G Y AA2 R MAGYARS M AE1 G Y AA2 R Z @@ -72868,6 +73259,7 @@ MAHATHIR M AE1 HH AH0 TH ER0 MAHATHIR(1) M AH0 HH AE1 TH IH2 R MAHATHIR(2) M AH0 HH AA1 TH IH2 R MAHATMA M AH0 HH AA1 T M AH0 +MAHAVIRA M AA1 HH AA0 V IY2 R AA0 MAHAYANA M AA2 HH AH0 Y AA1 N AH0 MAHDI M AA1 D IY0 MAHE M EY1 HH IY0 @@ -73290,6 +73682,7 @@ MALOTT M AH0 L AA1 T MALOUF M AE1 L OW0 F MALOY M AE1 L OY0 MALPASS M AE1 L P AH0 S +MALPENSA M AE2 L P EH1 N S AA0 MALPHRUS M AE1 L F R AH0 S MALPRACTICE M AE0 L P R AE1 K T AH0 S MALPRACTICE(1) M AE0 L P R AE1 K T IH0 S @@ -73446,6 +73839,7 @@ MANDEVILLE M AE1 N D AH0 V IH2 L MANDI M AE1 N D IY0 MANDIBLE M AE1 N D AH0 B AH0 L MANDIBLE(1) M AE1 N D IH0 B AH0 L +MANDIBULAR M AE2 N D IH1 B Y UW0 L ER0 MANDICH M AE1 N D IH0 K MANDIE M AE1 N D IY0 MANDIGO M AA0 N D IY1 G OW0 @@ -73729,6 +74123,7 @@ MANUFACTURERS' M AE2 N AH0 F AE1 K CH ER0 ER0 Z MANUFACTURES M AE2 N Y AH0 F AE1 K CH ER0 Z MANUFACTURING M AE2 N Y AH0 F AE1 K CH ER0 IH0 NG MANUFACTURING'S M AE2 N Y AH0 F AE1 K CH ER0 IH0 NG Z +MANUMISSION M AA2 N UW0 M IH1 SH AH0 N MANURE M AH0 N UH1 R MANUS M EY1 N IH0 S MANUSCRIPT M AE1 N Y AH0 S K R IH2 P T @@ -73788,6 +74183,8 @@ MARA M AA1 R AH0 MARABELLA M AE2 R AH0 B EH1 L AH0 MARABLE M EH1 R AH0 B AH0 L MARABOU M EH1 R AH0 B UW2 +MARACA M AA2 R AA1 K AA0 +MARACAS M AA2 R AA1 K AA0 Z MARACLE M AA1 R AH0 K AH0 L MARADONA M AA2 R AH0 D OW1 N AH2 MARADONA'S M AA2 R AH0 D OW1 N AH2 Z @@ -74203,6 +74600,8 @@ MARKMAN M AA1 R K M AH0 N MARKO M AA1 R K OW0 MARKOFF M AA1 R K AO2 F MARKOS M AA1 R K OW0 Z +MARKOV M AA1 R K OW0 V +MARKOVIAN M AA2 R K OW1 V IY2 AH0 N MARKOVIC M AA1 R K AH0 V IH0 K MARKOVICH M AA1 R K AH0 V IH0 CH MARKOVITZ M AA1 R K AH0 V IH0 T S @@ -74639,6 +75038,7 @@ MASLANKA M AH0 S L AE1 NG K AH0 MASLEN M AE1 S AH0 L AH0 N MASLEY M AE1 S L IY0 MASLIN M AE1 Z L IH0 N +MASLO M AA1 S L AO0 MASLOW M AA1 S L OW0 MASLOWSKI M AH0 S L AO1 F S K IY0 MASLYUKOV M AE1 S L Y UW0 K AA2 V @@ -75197,6 +75597,7 @@ MAXI M AE1 K S IY0 MAXICARE M AE1 K S IY0 K EH1 R MAXIE M AE1 K S IY0 MAXILLA M AE0 K S IH1 L AH0 +MAXILLARY M AE0 K S IH1 L AH0 R IY2 MAXIM M AE1 K S AH0 M MAXIMA M AE1 K S AH0 M AH0 MAXIMAL M AE1 K S AH0 M AH0 L @@ -75383,6 +75784,7 @@ MCALEXANDER M AH0 K AE2 L IH0 G Z AE1 N D ER0 MCALINDEN M AH0 K L IH1 N D AH0 N MCALISTER M AH0 K AE1 L AH0 S T ER0 MCALLEN M AH0 K AO1 L AH0 N +MCALLESTER M AH0 K AE1 L AH0 S T ER0 MCALLISTER M AH0 K AE1 L AH0 S T ER0 MCALOON M AE1 K AH0 L UW2 N MCALPIN M AH0 K AE1 L P AH0 N @@ -76399,6 +76801,7 @@ MCWILLIAM M AH0 K W IH1 L Y AH0 M MCWILLIAMS M AH0 K W IH1 L Y AH0 M Z MCWRIGHT M AH0 K R AY1 T MCZEAL M AH0 K Z IY1 L +MD EH2 M D IY1 ME M IY1 MEA M IY1 MEACHAM M IY1 CH AH0 M @@ -76880,6 +77283,7 @@ MELCHERT M EH1 L CH ER0 T MELCHING M EH1 L CH IH0 NG MELCHIOR M EY0 L CH IY1 ER0 MELCHIORRE M EH0 L K IY0 AO1 R EY0 +MELCHIZEDEK M EH2 K IY1 Z EH0 D EH2 K MELCHOR M EH1 L CH ER0 MELD M EH1 L D MELDED M EH1 L D AH0 D @@ -76951,6 +77355,7 @@ MELLING M EH1 L IH0 NG MELLINGER M EH1 L IH0 NG ER0 MELLIS M EH1 L IH0 S MELLISH M EH1 L IH0 SH +MELLITUS M EH1 L IH0 T AH0 S MELLMAN M EH1 L M AH0 N MELLO M EH1 L OW0 MELLOAN M EH0 L OW1 N @@ -77265,7 +77670,8 @@ MERCEDESES(1) M ER2 S EY1 D IY2 Z MERCENARIES M ER1 S AH0 N EH2 R IY0 Z MERCENARY M ER1 S AH0 N EH2 R IY0 MERCER M ER1 S ER0 -MERCHANDISE M ER1 CH AH0 N D AY2 Z +MERCHANDISE M ER1 CH AH0 N D AY2 S +MERCHANDISE(1) M ER1 CH AH0 N D AY2 Z MERCHANDISER M ER1 CH AH0 N D AY2 Z ER0 MERCHANDISERS M ER1 CH AH0 N D AY2 Z ER0 Z MERCHANDISING M ER1 CH AH0 N D AY2 Z IH0 NG @@ -77556,6 +77962,8 @@ MET'S M EH1 T S METABOLIC M EH2 T AH0 B AA1 L IH0 K METABOLISM M AH0 T AE1 B AH0 L IH2 Z AH0 M METABOLISMS M AH0 T AE1 B AH0 L IH2 Z AH0 M Z +METABOLITE M AH0 T AE1 B AH0 L AY2 T +METABOLITES M AH0 T AE1 B AH0 L AY2 T S METABOLIZE M AH0 T AE1 B AH0 L AY2 Z METACARPAL M EH2 T AH0 K AA1 R P AH0 L METACARPALS M EH2 T AH0 K AA1 R P AH0 L Z @@ -77594,9 +78002,11 @@ METAPHORICALLY M EH2 T AH0 F AO1 R IH0 K L IY0 METAPHORS M EH1 T AH0 F AO0 R Z METAPHYSICAL M EH2 T AH0 F IH1 Z IH0 K AH0 L METAPHYSICS M EH2 T AH0 F IH1 Z IH0 K S +METASTASES M AH0 T AE1 S T EY2 S IY0 S METASTASIS M AH0 T AE1 S T AH0 S IH0 S METASTASIZE M AH0 T AE1 S T AH0 S AY2 Z METASTASIZED M AH0 T AE1 S T AH0 S AY2 Z D +METASTATIC M EH2 T AH0 S T AE1 T IH0 K METATHORAX M EH2 T AH0 TH AO1 R AE2 K S METAVSKY M AH0 T AE1 V S K IY0 METAXAS M AH0 T AE1 K S AH0 S @@ -79288,6 +79698,7 @@ MITIGATION M IH2 T IH0 G EY1 SH AH0 N MITMAN M IH1 T M AH0 N MITNICK M IH1 T N IH0 K MITOCHONDRIA M AY2 T AH0 K AA1 N D R IY0 AH0 +MITOCHONDRIAL M AY2 T AH0 K AA1 N D R IY0 AH0 L MITOCHONDRION M AY2 T AH0 K AA1 N D R IY0 AH0 N MITOFSKY M IH0 T AO1 F S K IY0 MITOTIC M AY2 T AO1 T IH0 K @@ -79559,6 +79970,8 @@ MODULATION M AA2 JH AH0 L EY1 SH AH0 N MODULATOR M AA1 JH AH0 L EY2 T ER0 MODULE M AA1 JH UW0 L MODULES M AA1 JH UW0 L Z +MODULO M AO1 JH UW0 L OW2 +MODULUS M AO1 JH UW0 L UH0 S MODUS M OW1 D AH0 S MODUS-OPERANDI M OW1 D AH0 S AO2 P ER0 AE1 N D IY0 MODY M OW1 D IY0 @@ -79647,6 +80060,7 @@ MOHRMANN M AO1 R M AH0 N MOHS M AA1 S MOHTASHEMI M OW2 T AH0 SH EY1 M IY0 MOI M W AA1 +MOIETY M OY1 AH0 T IY2 MOILANEN M OY1 L AH0 N AH0 N MOINA M OY1 N AH0 MOINES M OY1 N Z @@ -79680,6 +80094,7 @@ MOLAISON M AH0 L EY1 Z AH0 N MOLAND M AA1 L AH0 N D MOLANDER M AA1 L AH0 N D ER0 MOLANO M OW0 L AA1 N OW0 +MOLAR M OW1 L AH0 R MOLASSES M AH0 L AE1 S AH0 Z MOLCHAN M OW1 L CH AH0 N MOLD M OW1 L D @@ -79713,6 +80128,8 @@ MOLENDA M OW0 L EH1 N D AH0 MOLER M OW1 L ER0 MOLES M OW1 L Z MOLESKI M AH0 L EH1 S K IY0 +MOLESKIN M AO1 L EH0 S K IH2 N +MOLESKINE M AO1 L EH0 S K IH2 N MOLESKY M AH0 L EH1 S K IY0 MOLEST M AH0 L EH1 S T MOLESTATION M OW2 L EH0 S T EY1 SH AH0 N @@ -79978,6 +80395,7 @@ MONOGAMY M AH0 N AA1 G AH0 M IY0 MONOGRAM M AA1 N AH0 G R AE2 M MONOGRAMMED M AA1 N AH0 G R AE2 M D MONOGRAPH M AA1 N AH0 G R AE2 F +MONOGRAPHS M AA1 N AH0 G R AE2 F S MONOHULL M AA1 N AH0 HH AH0 L MONOLINGUAL M AA2 N AH0 L IH1 NG G W AH0 L MONOLITH M AA1 N AH0 L IH2 TH @@ -80792,6 +81210,7 @@ MOTZKO M AA1 T S K OW0 MOUA M AW1 AH0 MOUDRY M OW1 D R IY0 MOUDY M AW1 D IY0 +MOUE M UW1 MOUEIX M UW2 W AY1 K S MOUL M AW1 L MOULD M OW1 L D @@ -80964,6 +81383,7 @@ MR. M IH1 S T ER0 MRAZ M R AE1 Z MRAZEK M R AA1 Z EH0 K MRAZIK M R AA1 Z IH0 K +MRI EH2 M AA2 R AY1 MROCZEK M R AA1 CH EH0 K MROCZKA M R AA1 CH K AH0 MROCZKOWSKI M R AH0 CH K AO1 F S K IY0 @@ -81269,6 +81689,8 @@ MULTITUDE(1) M AH1 L T AH0 T Y UW2 D MULTITUDES M AH1 L T AH0 T Y UW2 D Z MULTIUSER M AH1 L T IY0 Y UW2 Z ER0 MULTIVALVE M AH1 L T IY0 V AE0 L V +MULTIVARIATE M AH2 L T IY0 V AE1 R IY0 IH0 T +MULTIVARIATES M AH2 L T IY0 V AE1 R IY0 IH0 T S MULTIVISION M AH2 L T IY0 V IH1 ZH AH0 N MULTIVITAMIN M AH2 L T IY0 V AY1 T AH2 M AH0 N MULTIYEAR M AH1 L T IY0 Y IY1 R @@ -81959,6 +82381,7 @@ NALLS N AO1 L Z NALLY N AE1 L IY0 NAM N AE1 M NAMARA N AH0 M AA1 R AH0 +NAMASTE N AA1 M AA0 S T EY2 NAMBI N AE1 M B IY0 NAMBLA N AE1 M B L AH0 NAMBY-PAMBY N AE1 M B IY0 P AE1 M B IY0 @@ -82656,6 +83079,7 @@ NEIDLINGER(1) N IY1 D L IH0 NG ER0 NEIER N AY1 ER0 NEIFERT N IY1 F ER0 T NEIGER N AY1 G ER0 +NEIGH N EY1 NEIGHBOR N EY1 B ER0 NEIGHBOR'S N EY1 B ER0 Z NEIGHBORHOOD N EY1 B ER0 HH UH2 D @@ -82766,6 +83190,8 @@ NEOCONSERVATIVES N IY2 OW0 K AH0 N S ER1 V AH0 T IH0 V Z NEOLA N IY0 AA1 L AH0 NEOLIBERAL N IY2 OW0 L IH1 B ER0 AH0 L NEOLIBERALS N IY2 OW0 L IH1 B ER0 AH0 L Z +NEOLITH N IY1 OW0 L IH2 TH +NEOLITHIC N IY1 OW0 L IH2 TH IH0 K NEOMA N EY0 OW1 M AH0 NEON N IY1 AA0 N NEONATAL N IY2 OW0 N EY1 T AH0 L @@ -83780,6 +84206,7 @@ NIZHNY N IH1 ZH N IY0 NIZIOLEK N IH0 Z IY0 OW1 L EH0 K NIZNIK N IH1 Z N IH0 K NKOHSE EH0 NG K OW1 S IY0 +NMR EH2 N EH2 M AA1 R NO N OW1 NO'S N OW1 Z NO-BRAINER N OW2 B R EY1 N ER0 @@ -84119,6 +84546,7 @@ NON-WHITES(1) N AA1 N HH W AY1 T S NON-WOVEN N AA1 N W OW1 V IH0 N NON-WOVENS N AA1 N W OW1 V AH0 N Z NON-ZERO N AA0 N Z IY1 R OW0 +NON-ZERO-SUM N AA0 N Z IY1 R OW0 S AH2 M NONA N OW1 N AA0 NONACADEMIC N AA0 N AE2 K AH0 D EH1 M IH0 K NONACCRUAL N AA2 N AH0 K R UW1 AH0 L @@ -84565,6 +84993,8 @@ NOTED N OW1 T AH0 D NOTED(1) N OW1 T IH0 D NOTEHOLDER N OW1 T HH OW2 L D ER0 NOTEHOLDERS N OW1 T HH OW2 L D ER0 Z +NOTEPAD N OW1 T P AE2 D +NOTEPADS N OW1 T P AE2 D Z NOTES N OW1 T S NOTES' N OW1 T S NOTESTINE N OW0 T EH0 S T IY1 N IY0 @@ -84599,6 +85029,7 @@ NOTIFY N OW1 T AH0 F AY2 NOTIFYING N OW1 T AH0 F AY2 IH0 NG NOTING N OW1 T IH0 NG NOTION N OW1 SH AH0 N +NOTIONAL N OW1 SH AH0 N AH0 L NOTIONS N OW1 SH AH0 N Z NOTIS N OW1 T IH0 S NOTO N OW1 T OW0 @@ -84745,6 +85176,7 @@ NUCLEOLI N UW1 K L IY0 OW2 L IY0 NUCLEONIC N UW2 K L IY0 AA1 N IH0 K NUCLEONICS N UW2 K L IY0 AA1 N IH0 K S NUCLEOTIDE N UW1 K L IY0 AH0 T AY2 D +NUCLEOTIDES N UW1 K L IY0 AH0 T AY2 D Z NUCLEUS N UW1 K L IY0 AH0 S NUCOR N UW1 K AO2 R NUCOR'S N UW1 K AO2 R Z @@ -84808,6 +85240,9 @@ NUMED N UW0 M EH1 D NUMEIRI N UW0 M EY1 R IY0 NUMERAL N UW1 M ER0 AH0 L NUMERALS N UW1 M ER0 AH0 L Z +NUMERATE N UW1 M AH0 R AH0 T +NUMERATOR N UW1 M AH0 R EY2 T ER0 +NUMERATORS N UW1 M AH0 R EY2 T ER0 Z NUMERIC N UW0 M EH1 R IH0 K NUMERICA N UW0 M EH1 R IH0 K AH0 NUMERICAL N UW0 M EH1 R AH0 K AH0 L @@ -84826,6 +85261,7 @@ NUN N AH1 N NUN'S N AH1 N Z NUNAMAKER N UW0 N AA1 M EY0 K ER0 NUNAN N UW1 N AA0 N +NUNAVUT N UH1 N AH0 V AH0 T NUNCIO N AH1 N S IY0 OW0 NUNEMAKER N UW1 N M EY2 K ER0 NUNES N UW1 N Z @@ -84972,7 +85408,9 @@ NYLUND N AY1 L AH0 N D NYMAN N AY1 M AH0 N NYMEX N AY1 M EH2 K S NYMPH N IH1 M F +NYMPHO N IH2 M F OW0 NYMPHOMANIAC N IH2 M F OW0 M EY1 N IY0 AE2 K +NYMPHOMANIAC'S N IH2 M F OW0 M EY1 N IY0 AE2 K S NYMPHOMANIACS N IH2 M F OW0 M EY1 N IY0 AE2 K S NYMPHS N IH1 M F S NYNEX N AY1 N EH2 K S @@ -85275,6 +85713,7 @@ OBLITERATION AH0 B L IH2 T ER0 EY1 SH AH0 N OBLIVION AH0 B L IH1 V IY0 AH0 N OBLIVIOUS AH0 B L IH1 V IY0 AH0 S OBLONG AA1 B L AO0 NG +OBLOQUY AO1 B L AH0 K W IY2 OBNOXIOUS AA0 B N AA1 K SH AH0 S OBOE OW1 B OW0 OBOIST OW1 B OW0 AH0 S T @@ -85312,6 +85751,7 @@ OBSERVANCES AH0 B Z ER1 V AH0 N S IH0 Z OBSERVANT AH0 B Z ER1 V AH0 N T OBSERVATEUR AA0 B Z ER2 V AH0 T UH1 R OBSERVATION AA2 B Z ER0 V EY1 SH AH0 N +OBSERVATIONAL AA2 B Z ER0 V EY1 SH AH0 N AH0 L OBSERVATIONS AA2 B Z ER0 V EY1 SH AH0 N Z OBSERVATORIES AH0 B Z ER1 V AH0 T AO2 R IY0 Z OBSERVATORY AH0 B Z ER1 V AH0 T AO2 R IY0 @@ -85489,6 +85929,7 @@ OCTOPI AA1 K T AH0 P AY0 OCTOPUS AA1 K T AH0 P UH2 S OCTUPLET AA0 K T AH1 P L AH0 T OCTUPLETS AA0 K T AH1 P L AH0 T S +OCULAR AO1 K Y UW0 L ER0 OCULIST AO1 K Y UW0 L IH2 S T ODA OW1 D AH0 ODAIKO OW0 D EY1 K OW0 @@ -85575,6 +86016,7 @@ ODWYER OW0 D W AY1 ER0 ODYSSEUS OW0 D IH1 S IY0 AH0 S ODYSSEY AA1 D AH0 S IY0 ODYSSEY'S AA1 D AH0 S IY0 Z +OECD OW2 IY2 S IY2 D IY1 OEDIPAL EH1 D AH0 P AH0 L OEDIPUS EH1 D IH0 P AH0 S OEHLER OW1 L ER0 @@ -86094,6 +86536,7 @@ OLYMPICS OW0 L IH1 M P IH0 K S OLYMPICS' OW0 L IH1 M P IH0 K S OLYMPUS OW0 L IH1 M P AH0 S OLYMPUS' OW0 L IH1 M P AH0 S +OM AO1 M OMA OW1 M AH0 OMAAR OW1 M AA0 R OMAHA OW1 M AH0 HH AA2 @@ -86542,6 +86985,7 @@ ORBIN AO1 R B IH0 N ORBIS AO1 R B IH0 S ORBIT AO1 R B AH0 T ORBITAL AO1 R B AH0 T AH0 L +ORBITALS AO1 R B AH0 T AH0 L S ORBITED AO1 R B AH0 T AH0 D ORBITER AO1 R B AH0 T ER0 ORBITERS AO1 R B AH0 T ER0 Z @@ -87097,6 +87541,7 @@ OTHERWORLDLY AH1 DH ER0 W ER1 L D L IY0 OTHILIA OW0 TH IY1 L IY0 AH0 OTHMAN AA1 TH M AH0 N OTHMAN'S AA1 TH M AH0 N Z +OTIOSE OW1 T IY0 OW2 S OTIS OW1 T IH0 S OTMAR AA1 T M AA0 R OTOLOGIES OW0 T AA1 L AH0 JH IY0 Z @@ -87907,7 +88352,9 @@ OXFORD'S AA1 K S F ER0 D Z OXFORDS AA1 K S F ER0 D Z OXIDANT AA1 K S AH0 D AH0 N T OXIDANTS AA1 K S IH0 D AH0 N T S +OXIDASE AA1 K S IH0 D EY2 Z OXIDATION AA2 K S AH0 D EY1 SH AH0 N +OXIDATIVE AA2 K S AH0 D AY1 T IH0 V OXIDE AA1 K S AY2 D OXIDES AA1 K S AY2 D Z OXIDIZE AA1 K S AH0 D AY2 Z @@ -87922,6 +88369,7 @@ OXNER AA1 K S N ER0 OXOCO AA0 K S OW1 K OW0 OXTON AA1 K S T AH0 N OXY AA1 K S IY0 +OXYDATIVE AA2 K S AH0 D AY1 T IH0 V OXYGEN AA1 K S AH0 JH AH0 N OXYGEN(1) AA1 K S IH0 JH AH0 N OXYGENATE AA1 K S AH0 JH AH0 N EY2 T @@ -88290,6 +88738,7 @@ PALETTE P AE1 L AH0 T PALEY P EY1 L IY0 PALFREY P AE1 L F R IY0 PALIMONY P AE1 L IH0 M OW2 N IY0 +PALIMPSEST P AE1 L IH0 S EH2 S T PALIN P AE1 L IH0 N PALINKAS P AE1 L IH0 NG K AH0 Z PALISADE P AE2 L IH0 S EY1 D @@ -88822,6 +89271,7 @@ PARENTS P EH1 R AH0 N T S PARENTS' P EH1 R AH0 N T S PARES P EH1 R Z PARETI P EH2 R EH1 T IY0 +PARETO P AA2 R EH1 T OW0 PARETTI P EH2 R EH1 T IY0 PARFAIT P AA2 R F EY1 PARFAITS P AA2 R F EY1 Z @@ -89310,8 +89760,10 @@ PATHFINDER P AE1 TH F AY2 N D ER0 PATHMARK P AE1 TH M AA2 R K PATHMARK'S P AE1 TH M AA2 R K S PATHOGEN P AE1 TH AH0 JH AH0 N +PATHOGENESIS P AE2 TH AH0 JH EH1 N AH0 S IH0 S PATHOGENIC P AE2 TH AH0 JH EH1 N IH0 K PATHOGENS P AE1 TH AH0 JH AH0 N Z +PATHOLOGIC P AE2 TH AH0 L AA1 JH IH0 K PATHOLOGICAL P AE2 TH AH0 L AA1 JH IH0 K AH0 L PATHOLOGICALLY P AE2 TH AH0 L AA1 JH IH0 K L IY0 PATHOLOGIES P AH0 TH AA1 L AH0 JH IY0 Z @@ -89344,6 +89796,7 @@ PATMAN P AE1 T M AH0 N PATMORE P AE1 T M AO0 R PATNAUDE P AA0 T N AO1 D IY0 PATNODE P AE1 T N OW2 D +PATOIS P AE2 T W AA1 PATON P AE1 T AH0 N PATONS P AE1 T AH0 N Z PATRIARCA P AA0 T R IY0 AA1 R K AH0 @@ -89612,6 +90065,7 @@ PAZUTO P AH0 Z UW1 T OW0 PC P IY1 S IY1 PC'S P IY1 S IY1 Z PCS P IY1 S IY1 Z +PDF P IY2 D IY2 EH1 F PEA P IY1 PEABODY P IY1 B AA2 D IY0 PEABODY'S P IY1 B AA2 D IY0 Z @@ -89759,8 +90213,10 @@ PEDALING P EH1 D AH0 L IH0 NG PEDALING(1) P EH1 D L IH0 NG PEDALLED P EH1 D AH0 L D PEDALS P EH1 D AH0 L Z +PEDANT P EH1 D AH0 N T PEDANTIC P AH0 D AE1 N T IH0 K PEDANTRY P EH1 D AH0 N T R IY0 +PEDANTS P EH1 D AH0 N T S PEDDICORD P EH1 D IH0 K AO0 R D PEDDIE P EH1 D IY0 PEDDLE P EH1 D AH0 L @@ -90145,6 +90601,7 @@ PENSIONER P EH1 N SH AH0 N ER0 PENSIONERS P EH1 N SH AH0 N ER0 Z PENSIONS P EH1 N SH AH0 N Z PENSIVE P EH1 N S IH0 V +PENSIVENESS P EH1 N S IH0 V N EH2 S PENSKE P EH1 N S K IY0 PENSON P EH1 N S AH0 N PENSYL P EH1 N S IH0 L @@ -90366,6 +90823,7 @@ PERFUMED P ER0 F Y UW1 M D PERFUMES P ER0 F Y UW1 M Z PERFUMING P ER0 F Y UW1 M IH0 NG PERFUNCTORY P ER0 F AH1 NG K T ER0 IY0 +PERFUSION P ER0 F Y UW1 Z AH0 N PERGAMON P ER1 G AH0 M AH0 N PERGANDE P ER1 G IH0 N D PERGOLA P ER1 G AH0 L AH0 @@ -90421,6 +90879,7 @@ PERISHED P EH1 R IH0 SH T PERISHING P EH1 R IH0 SH IH0 NG PERISTYLE P EH1 R AH0 S T AY2 L PERITO P EH2 R IY1 T OW0 +PERITONEAL P EH2 R IY0 T OW0 N IY1 AH0 L PERJURE P ER1 JH ER0 PERJURED P ER1 JH ER0 D PERJURER P ER1 JH ER0 ER0 @@ -90990,6 +91449,7 @@ PEWS P Y UW1 Z PEWTER P Y UW1 T ER0 PEYMAN P EY1 M AH0 N PEYOT P EY1 AO0 T +PEYOTE P IH0 Y AO1 T IY2 PEYRELEVADE P EH2 R EH1 L AH0 V EY2 D PEYSER P EY1 Z ER0 PEYTON P EY1 T AH0 N @@ -91044,6 +91504,7 @@ PFUNDSTEIN F AH1 N D S T IY2 N PFUNDSTEIN(1) F AH1 N D S T AY2 N PGM P IY1 JH IY1 EH1 M PH P IY1 EY1 CH +PH.D. P IY1 EY2 CH D IY1 PHAGAN F EY1 G AH0 N PHAGE F EY1 JH PHAGOCYTE F AE1 G AH0 S AY2 T @@ -91075,6 +91536,8 @@ PHARAONIC F EH2 R AH0 AA1 N IH0 K PHARES F EH1 R Z PHARIS F AE1 R AH0 S PHARISAISM F AE1 R IH0 S EY2 IH2 Z AH0 M +PHARISEE F AA1 R IH0 S IY2 +PHARISEES F AA1 R IH0 S IY2 Z PHARISS F ER0 IH1 S PHARMA F AA1 R M AH0 PHARMACEUTICAL F AA2 R M AH0 S UW1 T IH0 K AH0 L @@ -91259,6 +91722,8 @@ PHLEGMATIC F L EH0 G M AE1 T IH0 K PHLOGOPITE F L AA1 G AH0 P AY2 T PHNOM F N AA1 M PHNOM(1) P AH0 N AA1 M +PHO F UH1 +PHO(1) F OW1 PHOBIA F OW1 B IY0 AH0 PHOBIAS F OW1 B IY0 AH0 Z PHOBIC F OW1 B IH0 K @@ -91380,6 +91845,7 @@ PHYSICISTS F IH1 Z IH0 S IH0 S T S PHYSICS F IH1 Z IH0 K S PHYSICS' F IH1 S IH0 K S PHYSIO F IH1 Z IY0 OW0 +PHYSIOLOGIC F IH2 Z IY0 AH0 L AA1 JH IH0 K PHYSIOLOGICAL F IH2 Z IY0 AH0 L AA1 JH IH0 K AH0 L PHYSIOLOGICALLY F IH2 Z IY0 AH0 L AA1 JH IH0 K L IY0 PHYSIOLOGIST F IH2 Z IY0 AA1 L AH0 JH IH0 S T @@ -91425,6 +91891,7 @@ PICA P AY1 K AH0 PICABO P AH0 K AA1 B OW0 PICANTE P IY0 K AA1 N T EY0 PICARD P IH0 K AA1 R D +PICARESQUE P IH2 K AH0 R EH1 S K PICARIELLO P IY0 K AA0 R IY0 EH1 L OW0 PICARO P IY1 K AA0 R OW2 PICAS P AY1 K AH0 Z @@ -92056,6 +92523,8 @@ PITMAN P IH1 T M AH0 N PITNER P IH1 T N ER0 PITNEY P IH1 T N IY0 PITOFSKY P AH0 T AA1 F S K IY0 +PITON P IY1 T AO2 N +PITONS P IY1 T AO2 N Z PITRE P AY1 T ER0 PITS P IH1 T S PITSCH P IH1 CH @@ -92355,6 +92824,7 @@ PLAUSIBILITY P L AO2 Z IH0 B IH1 L IH0 T IY0 PLAUSIBLE P L AO1 Z AH0 B AH0 L PLAUSIBLY P L AO1 Z AH0 B L IY0 PLAUT P L AO1 T +PLAUTUS P L AW1 T AH0 S PLAUTZ P L AO1 T S PLAVSIC P L AE1 V S IH0 K PLAVSIC'S P L AE1 V S IH0 K S @@ -92483,6 +92953,7 @@ PLETHORA(1) P L AH0 TH AO1 R AH0 PLETSCHER P L EH1 CH ER0 PLETT P L EH1 T PLETZ P L EH1 T S +PLEURAL P L UH1 R AH0 L PLEURISY P L UH1 R AH0 S IY0 PLEURITIDES P L UH2 R IH1 D AH0 T IY2 Z PLEURITIS P L UH2 R AY1 T AH0 S @@ -92585,6 +93056,8 @@ PLUFF P L AH1 F PLUG P L AH1 G PLUGGED P L AH1 G D PLUGGING P L AH1 G IH0 NG +PLUGIN P L AH1 G IH0 N +PLUGINS P L AH1 G IH0 N Z PLUGS P L AH1 G Z PLUM P L AH1 M PLUM'S P L AH1 M Z @@ -92753,7 +93226,9 @@ POESCHL P OW1 S K AH0 L POET P OW1 AH0 T POET'S P OW1 AH0 T S POETIC P OW0 EH1 T IH0 K +POETICAL P OW0 EH1 T IH0 K AH0 L POETICALLY P OW0 EH1 T IH0 K L IY0 +POETICS P OW0 EH1 T IH0 K S POETRY P OW1 AH0 T R IY0 POETS P OW1 AH0 T S POFAHL P AA1 F AA0 L @@ -93088,6 +93563,7 @@ POLYMORPHISM P AA2 L IY2 M AO1 R F IH0 Z M POLYNESIA P AA2 L IH2 N IY1 ZH AH0 POLYNESIAN P AA2 L IH2 N IY1 ZH AH0 N POLYNOMIAL P AA2 L IY2 N OW1 M IY0 AH0 L +POLYNOMIALS P AA2 L IY2 N OW1 M IY0 AH0 L Z POLYOLEFIN P AA2 L IY2 OW1 L AH0 F IH0 N POLYP P AA1 L IH0 P POLYPHASE P AA1 L IY2 F EY2 Z @@ -93229,6 +93705,7 @@ POOF P UW1 F POOH P UW1 POOH-BAH P UW1 B AH1 POOHED P UW1 D +POOKIE P UW1 K IY2 POOL P UW1 L POOL'S P UW1 L Z POOL-SIDE P UW1 L S AY1 D @@ -93288,6 +93765,7 @@ POPKO P OW1 P K OW0 POPLAR P AA1 P L ER0 POPLAWSKI P AH0 P L AA1 F S K IY0 POPLIN P AA1 P L IH0 N +POPLINS P AA1 P L IH0 N Z POPOFF P AA1 P AO2 F POPOLARE P AA2 P OW0 L AA1 R IY0 POPOV P OW1 P AH0 V @@ -93296,6 +93774,7 @@ POPOVICH P AA1 P AH0 V IH0 CH POPOWSKI P AH0 P AO1 F S K IY0 POPP P AA1 P POPPA P AA1 P AH0 +POPPADOM P AO1 P AH0 D AO2 M POPPE P AA1 P POPPEA P AA1 P IY0 AH0 POPPED P AA1 P T @@ -93452,6 +93931,7 @@ PORTLAND'S P AO1 R T L AH0 N D Z PORTLOCK P AO1 R T L AA2 K PORTLY P AO1 R T L IY0 PORTMAN P AO1 R T M AH0 N +PORTMANTEAU P AO1 R T M AA0 N T OW2 PORTNER P AO1 R T N ER0 PORTNEY P AO1 R T N IY0 PORTNOY P AO1 R T N OY0 @@ -93490,6 +93970,10 @@ POSEN P OW1 Z AH0 N POSER P OW1 Z ER0 POSES P OW1 Z AH0 Z POSES(1) P OW1 Z IH0 Z +POSEUR P OW1 Z ER0 +POSEUR(1) P OW2 Z ER1 +POSEURS P OW1 Z ER0 Z +POSEURS(1) P OW2 Z ER1 Z POSEY P OW1 Z IY0 POSH P AA1 SH POSHARD P AA1 SH ER0 D @@ -93589,9 +94073,11 @@ POSTMARKED P OW1 S T M AA2 R K T POSTMASTER P OW1 S T M AE2 S T ER0 POSTMASTERS P OW1 S T M AE2 S T ER0 Z POSTMODERN P OW0 S T M AA1 D ER0 N +POSTMODERNISM P OW2 S T M AA1 D ER0 N IH2 Z M POSTMORTEM P OW0 S T M AO1 R T EH0 M POSTNATAL P OW1 S T N EY1 T AH0 L POSTON P OW1 S T AH0 N +POSTOPERATIVE P OW2 S T AO1 P AH0 R AH0 T IH0 V POSTPONE P OW0 S T P OW1 N POSTPONED P OW0 S T P OW1 N D POSTPONEMENT P OW0 S T P OW1 N M AH0 N T @@ -93608,6 +94094,7 @@ POSTSCRIPTS P OW1 S T S K R IH2 P T S POSTTRAUMATIC P OW2 S T R AO0 M AE1 T IH0 K POSTULATE P AA1 S CH AH0 L EY2 T POSTULATE(1) P AA1 S CH AH0 L AH0 T +POSTULATED P AA1 S CH AH0 L EY2 T IH0 D POSTULATES P AA1 S CH AH0 L EY2 T S POSTULATES(1) P AA1 S CH AH0 L AH0 T S POSTUM P OW1 S T AH0 M @@ -93672,6 +94159,7 @@ POTSDAM P AA1 T S D AE2 M POTSHOT P AA1 SH AA2 T POTSHOTS P AA1 CH AA2 T S POTT P AA1 T +POTTAGE P AA1 T AH0 JH POTTEBAUM P AA1 T B AW0 M POTTED P AA1 T IH0 D POTTEIGER P AA1 T AY0 G ER0 @@ -93829,6 +94317,7 @@ PRADA P R AA1 D AH0 PRADESH P R AH0 D EH1 SH PRADETTO P R AH0 D EH1 T OW0 PRADO P R AA1 D OW0 +PRAEGER P R EY1 G ER0 PRAETOR P R IY1 T ER0 PRAETORIAN P R IY0 T AO1 R IY0 AH0 N PRAGER P R EY1 G ER0 @@ -94212,6 +94701,7 @@ PREOCCUPATIONS P R IY0 AA2 K Y AH0 P EY1 SH AH0 N Z PREOCCUPIED P R IY0 AA1 K Y AH0 P AY2 D PREOCCUPIES P R IY0 AA1 K Y AH0 P AY2 Z PREOCCUPY P R IY0 AA1 K Y AH0 P AY2 +PREOPERATIVE P R IY2 AO1 P AH0 R AH0 T IH0 V PREORDAIN P R IY2 AO0 R D EY1 N PREORDAINED P R IY2 AO0 R D EY1 N D PREP P R EH1 P @@ -94729,6 +95219,8 @@ PRIOLO P R IY0 OW1 L OW0 PRIOR P R AY1 ER0 PRIORE P R IY0 AO1 R IY0 PRIORE(1) P R AY0 AO1 R AY0 +PRIORI P R AY0 AO1 R AY2 +PRIORI(1) P R IY0 AO1 R IY2 PRIORITIES P R AY0 AO1 R AH0 T IY0 Z PRIORITIZE P R AY0 AO1 R AH0 T AY2 Z PRIORITIZED P R AY0 AO1 R AH0 T AY2 Z D @@ -95617,6 +96109,7 @@ PSEUDONYMS S UW1 D AH0 N IH2 M Z PSEUDOPODIAL S UW2 D AH0 P OW1 D IY0 AH0 L PSEUDOSCIENCE S UW2 D OW0 S AY1 AH0 N S PSEUDOSCIENTIFIC S UW2 D OW0 S AY2 AH0 N T IH1 F IH0 K +PSHAW P SH AW1 PSHEW P SH UW1 PSI S AY1 PSILOCYBIN S AY2 L AH0 S AY1 B AH0 N @@ -95652,6 +96145,8 @@ PSYCHOLOGISTS S AY0 K AA1 L AH0 JH AH0 S T S PSYCHOLOGY S AY0 K AA1 L AH0 JH IY0 PSYCHOPATH S AY1 K OW0 P AE2 TH PSYCHOPATHIC S AY2 K AH0 P AE1 TH IH0 K +PSYCHOPATHOLOGIES S AY2 K AH0 P AE0 TH AO1 L AH0 G IY2 Z +PSYCHOPATHOLOGY S AY2 K AH0 P AE0 TH AO1 L AH0 G IY2 PSYCHOPATHS S AY1 K OW0 P AE2 TH S PSYCHOPATHY S AY0 K AA1 P AH0 TH IY0 PSYCHOSIS S AY0 K OW1 S AH0 S @@ -95670,7 +96165,9 @@ PTOLEMY T AA1 L AH0 M IY0 PTOMAINE T OW1 M EY0 N PTOMAINES T OW1 M EY0 N Z PTOVSKY P AH0 T AO1 V S K IY0 +PTSD P IY2 T IY1 EH2 S D IY1 PTY T AY1 +PTY(1) P IY2 T IY2 W AY1 PTYON T AY1 AO0 N PU P UW1 PUAT P Y UW1 AE0 T @@ -95839,6 +96336,7 @@ PULSAR P UH1 L S ER0 PULSAR'S P UH1 L S ER0 Z PULSATING P AH1 L S EY2 T IH0 NG PULSE P AH1 L S +PULSED P AH1 L S T PULSES P AH1 L S IH0 Z PULSIFER P AH1 L S IH0 F ER0 PULSING P AH1 L S IH0 NG @@ -96203,6 +96701,7 @@ PYROXENE(1) P AY1 R AA0 K S IY2 N PYRRHIC P IH1 R IH0 K PYSHER P IH1 SH ER0 PYTEL P IH1 T AH0 L +PYTHAGORAS P IH2 TH AH0 G AO1 R AA0 S PYTHAGOREAN P IH2 TH AH0 G AO1 R IY0 AH0 N PYTHAGORUS P IH0 TH AE1 G AH0 R AH0 S PYTHIA P IH1 TH IY0 AH0 @@ -96214,6 +96713,7 @@ Q'S K Y UW1 Z Q. K Y UW1 Q.'S K Y UW1 Z Q.S K Y UW1 Z +QAEDA K AY1 D AA0 QANA K AA1 N AH0 QANTAS K AE1 N T AH0 S QANTAS(1) K AA1 N T AH0 S @@ -96246,6 +96746,7 @@ QUAD K W AA1 D QUADE K W EY1 D QUADRA K W AE1 D R AH0 QUADRANT K W AA1 D R AH0 N T +QUADRATIC K W AA0 D R AA1 T IH0 K QUADRENNIAL K W AA0 D R EH1 N IY0 AH0 L QUADREX K W AA1 D R EH0 K S QUADRICEPS K W AA1 D R AH0 S EH2 P S @@ -96260,6 +96761,7 @@ QUAFF K W AA1 F QUAFFED K W AA1 F AH0 D QUAGLIA K W AE1 G L IY0 AH0 QUAGMIRE K W AE1 G M AY2 ER0 +QUAHOG K W AA1 HH AO2 G QUAI K EY1 QUAID K W EY1 D QUAIL K W EY1 L @@ -96484,6 +96986,7 @@ QUESTIONS' K W EH1 S CH AH0 N Z QUESTRAN K W EH1 S T R AE2 N QUESTROM K W EH1 S T R AH0 M QUESTS K W EH1 S T S +QUETZALCOATL K EH1 T S AA0 L K W AO2 T L QUEUE K Y UW1 QUEUES K Y UW1 Z QUEUING K Y UW1 IH0 NG @@ -96570,6 +97073,7 @@ QUINN K W IH1 N QUINN'S K W IH1 N Z QUINNELL K W IH1 N AH0 L QUINNEY K W IH1 N IY0 +QUINOA K IY2 N OW1 AH0 QUINOBEQUIN K W IH2 N OW1 B IH0 K W IH0 N QUINOCO K W IH0 N OW1 K OW0 QUINON K W IH1 N AH0 N @@ -96606,6 +97110,7 @@ QUIP K W IH1 P QUIPP K W IH1 P QUIPPED K W IH1 P T QUIPS K W IH1 P S +QUIPSTER K W IH1 P S T ER0 QUIRAM K W IY1 R AH0 M QUIRE K W AY1 R QUIRIN K W IH1 R IH0 N @@ -96646,6 +97151,8 @@ QUIZZES K W IH1 Z IH0 Z QUIZZICAL K W IH1 Z AH0 K AH0 L QUIZZING K W IH1 Z IH0 NG QUO K W OW1 +QUOD K W AO1 D +QUOIN K W OY1 N QUON K W AA1 N QUORA K W AO1 R AH0 QUORUM K W AO1 R AH0 M @@ -96685,6 +97192,7 @@ RABB R AE1 B RABBANI R AH0 B AE1 N IY0 RABBANI(1) R AH0 B AA1 N IY0 RABBI R AE1 B AY2 +RABBINIC R AH0 B IH1 N IH0 K RABBINICAL R AH0 B IH1 N IH0 K AH0 L RABBIS R AE1 B AY2 Z RABBIT R AE1 B AH0 T @@ -96865,6 +97373,7 @@ RADIOACTIVE R EY2 D IY0 OW0 AE1 K T IH0 V RADIOACTIVITY R EY1 D IY0 OW0 AE2 K T IH2 V AH0 T IY2 RADIOACTIVITY(1) R EY2 D IY0 OW0 AE2 K T IH1 V AH0 T IY2 RADIOED R EY1 D IY0 OW2 D +RADIOGRAPHIC R EY2 D IY0 OW2 G R AA1 F IH0 K RADIOGRAPHY R EY2 D IY0 AA1 G R AH0 F IY0 RADIOLOGICAL R EY2 D IY0 AH0 L AA1 JH IH0 K AH0 L RADIOLOGIST R EY2 D IY0 AA1 L AH0 JH IH0 S T @@ -97237,6 +97746,7 @@ RAMPAGES R AE1 M P EY2 JH IH0 Z RAMPAGING R AE1 M P EY2 JH IH0 NG RAMPAGING(1) R AE1 M P AH0 JH IH0 NG RAMPANT R AE1 M P AH0 N T +RAMPART R AE1 M P AA2 R T RAMPARTS R AE1 M P AA2 R T S RAMPELL R AE0 M P EH1 L RAMPEY R AE1 M P IY0 @@ -97804,6 +98314,7 @@ RAZORS R EY1 Z ER0 Z RAZZANO R AA0 T S AA1 N OW0 RAZZLE R AE1 Z AH0 L RAZZMATAZZ R AE1 Z M AH0 T AE1 Z +RCA AA2 R S IY2 EY1 RE R EY1 RE(1) R IY1 RE'S R EY1 Z @@ -98356,7 +98867,8 @@ RECOLLECTION R EH2 K AH0 L EH1 K SH AH0 N RECOLLECTIONS R EH2 K AH0 L EH1 K SH AH0 N Z RECOLLECTS R EH2 K AH0 L EH1 K T S RECOLLECTS(1) R IY2 K AH0 L EH1 K T S -RECOMBINANT R IH0 K AA1 M B IH0 N AH0 N T +RECOMBINANT R IY0 K AA1 M B IH0 N AH0 N T +RECOMBINATION R IY0 K AA2 M B IH0 N EY1 SH AH0 N RECOMBINE R IY2 K AH0 M B AY1 N RECOMMEND R EH2 K AH0 M EH1 N D RECOMMENDATION R EH2 K AH0 M AH0 N D EY1 SH AH0 N @@ -98395,6 +98907,7 @@ RECONNECTED R IY0 K AH0 N EH1 K T IH0 D RECONNECTING R IY0 K AH0 N EH1 K T IH0 NG RECONNECTS R IY0 K AH0 N EH1 K T S RECONNOITER R IY2 K AH0 N OY1 T ER0 +RECONNOITRE R IY2 K AH0 N OY1 T ER0 RECONQUER R IY0 K AO1 NG K ER0 RECONQUERED R IY0 K AO1 NG K ER0 D RECONSIDER R IY2 K AH0 N S IH1 D ER0 @@ -98524,6 +99037,7 @@ RECURRENT R IH0 K ER1 AH0 N T RECURRENT(1) R IY0 K ER1 AH0 N T RECURRING R IH0 K ER1 IH0 NG RECURRING(1) R IY0 K ER1 IH0 NG +RECURSIVE R IY2 K ER1 S IH0 V RECUSAL R IH0 K Y UW1 Z AH0 L RECUSE R IH2 K Y UW1 Z RECUSED R IH0 K Y UW1 Z D @@ -98571,6 +99085,7 @@ REDDING R EH1 D IH0 NG REDDINGER R EH1 D IH0 NG ER0 REDDINGTON R EH1 D IH0 NG T AH0 N REDDISH R EH1 D IH0 SH +REDDIT R EH1 D IH0 T REDDITT R EH1 D IH0 T REDDOCH R EH1 D AH0 K REDDY R EH1 D IY0 @@ -98911,6 +99426,7 @@ REFLEXES R IY1 F L EH0 K S AH0 Z REFLEXIVE R AH0 F L EH1 K S IH0 V REFLEXIVELY R IY0 F L EH1 K S IH0 V L IY0 REFLEXIVITY R IY2 F L EH2 K S IH1 V IH0 T IY0 +REFLUX R IY1 F L AH2 K S REFOCUS R IY0 F OW1 K AH0 S REFOCUSED R IY0 F OW1 K AH0 S T REFOCUSES R IY0 F OW1 K AH0 S IH0 Z @@ -99010,6 +99526,7 @@ REGAINING R IH0 G EY1 N IH0 NG REGAINS R IY0 G EY1 N Z REGAL R IY1 G AH0 L REGALADO R EY0 G AA0 L AA1 D OW0 +REGALE R IH0 G EY1 L REGALED R IH0 G EY1 L D REGALES R IH0 G EY1 L Z REGALIA R IH0 G EY1 L Y AH0 @@ -99921,6 +100438,7 @@ RENTERIA R EH0 N T EH1 R IY0 AH0 RENTERS R EH1 N T ER0 Z RENTFRO R EH1 N T F R OW0 RENTFROW R EH1 N T F R AW0 +RENTIER R EH1 T IY2 ER0 RENTING R EH1 N T IH0 NG RENTMEESTER R EH1 N T M IY2 S T ER0 RENTON R EH1 N T AH0 N @@ -100328,6 +100846,7 @@ RESEARCHERS R IY1 S ER0 CH ER0 Z RESEARCHERS' R IY1 S ER0 CH ER0 Z RESEARCHES R IY0 S ER1 CH IH0 Z RESEARCHING R IY0 S ER1 CH IH0 NG +RESECTION R IY2 S EH1 K SH AH0 N RESEDA R EH0 S EY1 D AH0 RESEED R IY0 S IY1 D RESELL R IY0 S EH1 L @@ -100464,6 +100983,7 @@ RESISTENCE R IH0 Z IH1 S T AH0 N S RESISTING R IH0 Z IH1 S T IH0 NG RESISTING(1) R IY0 Z IH1 S T IH0 NG RESISTIVENESS R IH2 Z IH1 S T IH2 V N AH0 S +RESISTOR R IH0 Z IH1 S T ER0 RESISTORS R IH0 Z IH1 S T ER0 Z RESISTS R IH0 Z IH1 S T S RESISTS(1) R IY0 Z IH1 S T S @@ -100959,6 +101479,7 @@ REVEALING R IH0 V IY1 L IH0 NG REVEALING(1) R IY0 V IY1 L IH0 NG REVEALS R IH0 V IY1 L Z REVEALS(1) R IY0 V IY1 L Z +REVEILLE R EH1 V AH0 L IY2 REVEL R EH1 V AH0 L REVELATION R EH2 V AH0 L EY1 SH AH0 N REVELATIONS R EH2 V AH0 L EY1 SH AH0 N Z @@ -101703,6 +102224,7 @@ RILWANU R IH0 L W AA1 N UW0 RIM R IH1 M RIMA R IY1 M AH0 RIMBEY R IH1 M B IY0 +RIME R AY1 M RIMEL R IH1 M AH0 L RIMER R AY1 M ER0 RIMES R AY1 M Z @@ -102038,6 +102560,7 @@ RIZZOLI R IH0 Z OW1 L IY0 RIZZOLO R IY0 T S OW1 L OW0 RIZZUTI R IY0 T S UW1 T IY0 RIZZUTO R IY0 T S UW1 T OW0 +RNA AA2 R EH2 N EY1 RO R OW1 ROA R OW1 AH0 ROACH R OW1 CH @@ -102768,6 +103291,7 @@ ROOST R UW1 S T ROOSTED R UW1 S T IH0 D ROOSTER R UW1 S T ER0 ROOSTERS R UW1 S T ER0 Z +ROOSTING R UW1 S T IH0 NG ROOT R UW1 T ROOTED R UW1 T AH0 D ROOTED(1) R UW1 T IH0 D @@ -103235,6 +103759,7 @@ ROWAN R OW1 AH0 N ROWAN'S R OW1 AH0 N Z ROWAND R OW1 AH0 N D ROWBOAT R OW1 B OW2 T +ROWBOATS R OW1 B OW2 T S ROWBOTHAM R OW1 B AH0 TH AE0 M ROWDEN R OW1 D AH0 N ROWDIES R AW1 D IY0 Z @@ -103508,6 +104033,7 @@ RUEL R UW1 L RUELAS R UW1 L AH0 Z RUELLA R UW2 EH1 L AH0 RUELLE R UW2 EH1 L +RUES R UW1 Z RUESCH R UW1 SH RUESS R UW1 S RUEST R UW1 S T @@ -104847,6 +105373,7 @@ SANFORD'S S AE1 N F ER0 D Z SANG S AE1 NG SANG-GON S AA1 NG G AO1 N SANGER S AE1 NG ER0 +SANGFROID S AA2 N F R AW1 SANGIOVESE S AE2 N JH IY1 OW0 V IY2 S SANGSTER S AE1 NG S T ER0 SANGUINE S AE1 NG G W IH0 N @@ -105173,6 +105700,8 @@ SATES S EY1 T S SATHER S AE1 DH ER0 SATHRE S AE1 TH ER0 SATHYAVAGISWARAN S AE0 TH Y AH0 V AA2 G IH0 S W AA2 R AH0 N +SATIATED S EY1 SH IY2 EY2 T IH0 D +SATIETY S AA0 T AY1 AH0 T IY2 SATIN S AE1 T AH0 N SATINS S AE1 T AH0 N Z SATIRE S AE1 T AY2 ER0 @@ -105449,6 +105978,7 @@ SAYS S EH1 Z SAYS(1) S IH1 Z SAYYID S AY1 IH0 D SAZAMA S AA0 Z AA1 M AH0 +SBA EH2 S B IY2 EY1 SBARRO S B AA1 R OW0 SBF EH1 S B IY1 EH1 F SCAB S K AE1 B @@ -106485,6 +107015,7 @@ SCHUTZMAN SH AH1 T Z M AH0 N SCHUUR SH UH1 R SCHUYLER S K AY1 L ER0 SCHUYLKILL S K Y UW1 L K IH2 L +SCHWA SH W AA1 SCHWAB SH W AA1 B SCHWAB'S SH W AA1 B Z SCHWABE SH W AO1 B @@ -106852,6 +107383,7 @@ SCREENWRITER S K R IY1 N R AY2 T ER0 SCREENWRITERS S K R IY1 N R AY2 T ER0 Z SCREENWRITING S K R IY1 N R AY2 T IH0 NG SCREW S K R UW1 +SCREW-UP S K R UW1 AH2 P SCREWBALL S K R UW1 B AO2 L SCREWDRIVER S K R UW1 D R AY2 V ER0 SCREWDRIVERS S K R UW1 D R AY2 V ER0 Z @@ -106934,6 +107466,7 @@ SCRUTON S K R UW1 T AH0 N SCRUTTON S K R AH1 T AH0 N SCRUTTON'S S K R AH1 T AH0 N Z SCS EH2 S S IY2 EH1 S +SCSI S K UH1 Z IY2 SCUBA S K UW1 B AH0 SCUD S K AH1 D SCUDDER S K AH1 D ER0 @@ -107178,6 +107711,7 @@ SEBASTIANI S AH0 B AE2 S T IY0 AA1 N IY0 SEBASTIANI(1) S AH0 B AE2 S T Y AA1 N IY0 SEBASTIANIS S AH0 B AE2 S T IY0 AA1 N IY0 Z SEBASTIANIS(1) S AH0 B AE2 S T Y AA1 N IY0 Z +SEBASTOPOL S EH0 B AA1 S T AH0 P AO2 L SEBBY S EH1 B IY0 SEBEK S EH1 B IH0 K SEBER S IY1 B ER0 @@ -107465,6 +107999,8 @@ SEGUNDO S EH2 G UH1 N D OW2 SEGUR S EY0 G UH1 R SEGURA S EY0 G UH1 R AH0 SEGUROS S EY2 G Y ER1 OW0 Z +SEGWAY S EH1 G W EY2 +SEGWAYS S EH1 G W EY2 Z SEHER S EH1 HH ER0 SEHNERT S EH1 N ER0 T SEHORN S EH1 HH ER0 N @@ -108040,6 +108576,8 @@ SEPARATISM S EH1 P ER0 AH0 T IH2 Z AH0 M SEPARATIST S EH1 P ER0 AH0 T IH0 S T SEPARATISTS S EH1 P ER0 AH0 T IH0 S T S SEPARATISTS(1) S EH1 P R AH0 T IH0 S T S +SEPARATOR S EH1 P AH0 R EY2 T ER0 +SEPARATORS S EH1 P AH0 R EY2 T ER0 Z SEPE S IY1 P SEPEDA S EY0 P EY1 D AH0 SEPHARDIC S AH0 F AA1 R D IH0 K @@ -108241,6 +108779,7 @@ SERVICING S ER1 V IH0 S IH0 NG SERVICO S ER1 V IH0 K OW2 SERVIDIO S ER0 V IY1 D IY0 OW0 SERVILE S ER1 V AH0 L +SERVILITY S ER1 V IH0 L AH0 T IY2 SERVIN S ER1 V IH0 N SERVING S ER1 V IH0 NG SERVINGS S ER1 V IH0 NG Z @@ -108321,6 +108860,7 @@ SEUBERT S UW1 B ER0 T SEUFERT S UW1 F ER0 T SEUSS S UW1 S SEVAREID S EH1 V AH0 R AY2 D +SEVASTOPOL S EH2 V AH0 S T AO1 P AH0 L SEVCIK S EH1 V S IH0 K SEVE S EH1 V EY0 SEVEN S EH1 V AH0 N @@ -108504,6 +109044,7 @@ SHAFTED SH AE1 F T IH0 D SHAFTING SH AE1 F T IH0 NG SHAFTS SH AE1 F T S SHAG SH AE1 G +SHAGGING SH AE1 G IH0 NG SHAGGY SH AE1 G IY0 SHAGING SH AE1 G IH0 NG SHAGS SH AE1 G Z @@ -108642,6 +109183,7 @@ SHANKLIN SH AE1 NG K L IH0 N SHANKMAN SH AE1 NG K M AH0 N SHANKS SH AE1 NG K S SHANLEY SH AE1 N L IY0 +SHANNA SH AE1 N AH0 SHANNAHAN SH AE1 N AH0 HH AE0 N SHANNON SH AE1 N AH0 N SHANNON'S SH AE1 N AH0 N Z @@ -109338,6 +109880,7 @@ SHIT SH IH1 T SHITHEAD SH IH1 T HH EH2 D SHITHOLE SH IH1 T HH OW2 L SHITILA SH AH0 T IH1 L AH0 +SHITLOAD SH IH1 T L OW2 D SHITS SH IH1 T SHITSTORM SH IH1 T S T AO2 R M SHITTING SH IH1 T IH0 NG @@ -110148,6 +110691,7 @@ SIGWALD S IH1 G W AH0 L D SIHANOUK S IY1 AH0 N UH2 K SIKES S AY1 K S SIKH S IY1 K +SIKHISM S IY1 K IH0 Z M SIKHS S IY1 K S SIKKEMA S IH0 K IY1 M AH0 SIKLIE S IH1 K L IY0 @@ -110546,6 +111090,7 @@ SIRIANNI S IH0 R IY0 AA1 N IY0 SIRIGNANO S IH2 R IY0 N Y AA1 N OW0 SIRIS S AY1 R IH0 S SIRIS(1) S IH1 R IH0 S +SIRIUS S IH1 R IH2 AH0 S SIRK S ER1 K SIRKIN S ER1 K IH0 N SIRKO S ER1 K OW0 @@ -110707,6 +111252,7 @@ SJOGREN SH OW1 G R AH0 N SJOLANDER SH OW1 L AE2 N D ER0 SJOQUIST SH OW1 K W IH0 S T SJOSTROM SH OW1 S T R AH0 M +SKA S K AA1 SKAAR S K AA1 R SKADDEN S K AE1 D IH0 N SKAFF S K AE1 F @@ -110994,6 +111540,7 @@ SLADKY S L AE1 D K IY0 SLAG S L AE1 G SLAGEL S L AE1 G AH0 L SLAGER S L EY1 G ER0 +SLAGGING S L AE1 G IH0 NG SLAGHT S L AE1 T SLAGLE S L EY1 G AH0 L SLAGTER S L AE1 G T ER0 @@ -111781,6 +112328,7 @@ SNOBS S N AA1 B Z SNODDERLY S N AA1 D ER0 L IY0 SNODDY S N AA1 D IY0 SNODGRASS S N AA1 D G R AE2 S +SNOG S N AO1 G SNOHOMISH S N AA1 HH AH0 M IH0 SH SNOKE S N OW1 K SNOOK S N UH1 K @@ -111797,6 +112345,7 @@ SNOOZING S N UW1 Z IH0 NG SNORE S N AO1 R SNORER S N AO1 R ER0 SNORERS S N AO1 R ER0 Z +SNORES S N AO1 R Z SNORING S N AO1 R IH0 NG SNORKEL S N AO1 R K AH0 L SNORKELING S N AO1 R K AH0 L IH2 NG @@ -111846,6 +112395,7 @@ SNOWPLOW S N OW1 P L AW2 SNOWPLOWS S N OW1 P L AW2 Z SNOWS S N OW1 Z SNOWSHOE S N OW1 SH UW2 +SNOWSHOES S N OW1 SH UW2 Z SNOWSTORM S N OW1 S T AO2 R M SNOWSTORMS S N OW1 S T AO2 R M Z SNOWY S N OW1 IY0 @@ -111862,6 +112412,7 @@ SNUFFS S N AH1 F S SNUG S N AH1 G SNUGGING S N AH1 G IH0 NG SNUGGLE S N AH1 G AH0 L +SNUGGLED S N AH1 G AH0 L D SNUGGS S N AH1 G Z SNUGLY S N AH1 G L IY0 SNYDER S N AY1 D ER0 @@ -112237,8 +112788,12 @@ SOLTI S OW1 L T IY0 SOLTIS S OW1 L T IH0 S SOLTYS S OW1 L T IY0 Z SOLTYSIAK S OW0 L T IH1 S IY0 AE0 K +SOLUBILITY S AA2 L Y AH0 B IH1 L AH0 T IY2 SOLUBLE S AA1 L Y AH0 B AH0 L +SOLUBLES S AA1 L Y AH0 B AH0 L Z SOLUM S OW1 L AH0 M +SOLUTE S AA1 L Y UW0 T +SOLUTES S AA1 L Y UW0 T S SOLUTION S AH0 L UW1 SH AH0 N SOLUTIONS S AH0 L UW1 SH AH0 N Z SOLVABLE S AA1 L V AH0 B AH0 L @@ -112269,6 +112824,7 @@ SOMALIAS S AH0 M AA1 L IY0 AH0 Z SOMALIAS(1) S AH0 M AA1 L Y AH0 Z SOMALILAND S AH0 M AA1 L IY0 L AE2 N D SOMALIS S AH0 M AA1 L IY0 Z +SOMATIC S OW2 M AA1 T IH0 K SOMATOGEN S OW2 M AE1 T AH0 JH EH0 N SOMATOTROPIN S OW2 M AH0 T AA1 T R AH0 P IH0 N SOMBER S AA1 M B ER0 @@ -112305,6 +112861,7 @@ SOMEWHAT(1) S AH1 M HH W AH1 T SOMEWHERE S AH1 M W EH2 R SOMEWHERES S AH1 M W EH2 R Z SOMMA S AA1 M AH0 +SOMME S AO1 M SOMMER S AH1 M ER0 SOMMERFELD S AA1 M ER0 F EH0 L D SOMMERFELDT S AA1 M ER0 F IH0 L T @@ -112428,6 +112985,7 @@ SOPHISTICATED S AH0 F IH1 S T AH0 K EY2 T IH0 D SOPHISTICATED(1) S AH0 F IH1 S T IH0 K EY2 T AH0 D SOPHISTICATES S AH0 F IH1 S T AH0 K IH2 T S SOPHISTICATION S AH0 F IH2 S T AH0 K EY1 SH AH0 N +SOPHISTRY S AO1 F IH0 S T R IY2 SOPHOCLES S AA1 F AH0 K L IY0 Z SOPHOMORE S AA1 F M AO2 R SOPHOMORES S AA1 F M AO2 R Z @@ -112545,6 +113103,8 @@ SOUDER S AW1 D ER0 SOUDERS S AW1 D ER0 Z SOUERS S AW1 ER0 Z SOUFFLE S UW0 F L EY1 +SOUGH S AW1 F +SOUGH(1) S OW1 SOUGHT S AO1 T SOUK S UW1 K SOUKUP S AW1 K AH0 P @@ -112637,6 +113197,7 @@ SOUTHLIFE S AW1 TH L AY2 F SOUTHMARK S AW1 TH M AA2 R K SOUTHMARK'S S AW1 TH M AA2 R K S SOUTHOLD S AW1 TH OW2 L D +SOUTHPAW S AW1 TH P AW2 SOUTHPORT S AW1 TH P AO2 R T SOUTHS S AW1 TH S SOUTHSIDE S AW1 TH S AY2 D @@ -113132,6 +113693,8 @@ SPHERE S F IH1 R SPHERES S F IH1 R Z SPHERICAL S F EH1 R IH0 K AH0 L SPHEROID S F IH1 R OY2 D +SPHINCTER S F IH1 NG K T ER0 +SPHINCTERS S F IH1 NG K T ER0 Z SPHINX S F IH1 NG K S SPIC S P IH1 K SPICE S P AY1 S @@ -113630,6 +114193,8 @@ SPYGLASS S P AY1 G L AE2 S SPYING S P AY1 IH0 NG SPYKER S P AY1 K ER0 SPYWARE S P AY1 W EH2 R +SQL EH2 S K Y UW2 EH1 L +SQL(1) S IY1 K W UH0 L SQUABBLE S K W AA1 B AH0 L SQUABBLED S K W AA1 B AH0 L D SQUABBLES S K W AA1 B AH0 L Z @@ -113644,6 +114209,7 @@ SQUALID S K W AA1 L AH0 D SQUALL S K W AO1 L SQUALLS S K W AO1 L Z SQUALOR S K W AA1 L ER0 +SQUAMOUS S K W EY1 M AH0 S SQUANDER S K W AA1 N D ER0 SQUANDERED S K W AA1 N D ER0 D SQUANDERING S K W AA1 N D ER0 IH0 NG @@ -113910,6 +114476,8 @@ STAKEOUTS S T EY1 K AW2 T S STAKER S T EY1 K ER0 STAKES S T EY1 K S STAKING S T EY1 K IH0 NG +STALAG S T AE1 L AE2 G +STALAGMITE S T AE1 L AH0 G M AY2 T STALCUP S T AO1 L K AH2 P STALDER S T AO1 L D ER0 STALE S T EY1 L @@ -114718,6 +115286,7 @@ STENNIS S T EH1 N IH0 S STENO S T EH1 N OW0 STENOGRAPHER S T EH0 N AH1 G R AH0 F ER0 STENOGRAPHIC S T EH2 N AH0 G R AE1 F IH0 K +STENOSIS S T EH2 N OW1 S IH0 S STENQUIST S T EH1 N K W IH2 S T STENSETH S T EH1 N S IH0 TH STENSLAND S T EH1 N S L AH0 N D @@ -115552,6 +116121,7 @@ STRATEGY'S S T R AE1 T AH0 JH IY0 Z STRATER S T R EY1 T ER0 STRATFORD S T R AE1 T F ER0 D STRATHMAN S T R AE1 TH M AH0 N +STRATIFICATION S T R AE2 T AH0 F IH0 K EY1 SH AH0 N STRATIFIED S T R AE1 T AH0 F AY2 D STRATIFY S T R AE1 T AH0 F AY2 STRATIGRAPHIC S T R AE2 T AH0 G R AE1 F IH0 K @@ -116272,6 +116842,7 @@ SUBPRINCIPALS S AH0 B P R IH1 N S AH0 P AH0 L Z SUBRA S UW1 B R AH0 SUBRAMANIAN S UW2 B R AH0 M AA1 N IY0 AH0 N SUBROTO S UW0 B R OW1 T OW0 +SUBROUTINE S AH1 B R UW0 T IY2 N SUBS S AH1 B Z SUBS'S S AH1 B Z IH0 Z SUBSAHARAN S AH2 B S AH0 HH EH1 R AH0 N @@ -116291,6 +116862,7 @@ SUBSEQUENTLY S AH1 B S AH0 K W AH0 N T L IY0 SUBSERVIENCE S AH0 B S ER1 V IY0 AH0 N S SUBSERVIENT S AH0 B S ER1 V IY0 AH0 N T SUBSET S AH1 B S EH2 T +SUBSETS S AH1 B S EH2 T S SUBSIDE S AH0 B S AY1 D SUBSIDED S AH0 B S AY1 D IH0 D SUBSIDENCE S AH0 B S AY1 D AH0 N S @@ -116360,6 +116932,8 @@ SUBTRACTING S AH0 B T R AE1 K T IH0 NG SUBTRACTION S AH0 B T R AE1 K SH AH0 N SUBTYPE S AH1 B T AY2 P SUBTYPING S AH1 B T AY2 P IH0 NG +SUBUNIT S AH2 B Y UW1 N IH0 T +SUBUNITS S AH2 B Y UW1 N IH0 T S SUBURB S AH1 B ER0 B SUBURB'S S AH1 B ER0 B Z SUBURBAN S AH0 B ER1 B AH0 N @@ -117780,6 +118354,7 @@ SYME S AY1 M SYMES S AY1 M Z SYMINGTON S IH1 M IH0 NG T AH0 N SYMMES S IH1 M Z +SYMMETRIC S AH0 M EH1 T R IH0 K SYMMETRICAL S AH0 M EH1 T R IH0 K AH0 L SYMMETRICALLY S AH0 M EH1 T R IH0 K L IY0 SYMMETRY S IH1 M AH0 T R IY0 @@ -117820,6 +118395,7 @@ SYNALLOY'S S IH0 N AE1 L OY0 Z SYNAN S AY1 N AH0 N SYNAPSE S IH1 AE0 P S SYNAPSES S IH1 AE0 P S IH0 Z +SYNAPTIC S IH2 N AE1 P T IH0 K SYNAR S IH1 N AA0 R SYNAR(1) S AY1 N AA0 R SYNBIOTICS S IH2 N B IY0 AA1 T IH0 K S @@ -117834,6 +118410,7 @@ SYNCHRONIZE S IH1 NG K R AH0 N AY2 Z SYNCHRONIZED S IH1 NG K R AH0 N AY2 Z D SYNCHRONIZES S IH1 NG K R AH0 N AY2 Z IH0 Z SYNCHRONIZING S IH1 NG K R AH0 N AY2 Z IH0 NG +SYNCHRONOUS S IH1 NG K R AH0 N AH0 S SYNCOM S IH1 NG K AA0 M SYNCOPAL S IH1 N K AH0 P AH2 SYNCOPATE S IH1 NG K AH0 P EY2 T @@ -117873,11 +118450,13 @@ SYNOD'S S IH1 N AH0 D Z SYNONYM S IH1 N AH0 N IH2 M SYNONYMOUS S AH0 N AA1 N AH0 M AH0 S SYNONYMOUSLY S AH0 N AA1 N AH0 M AH0 S L IY0 +SYNONYMS S IH1 N AH0 N IH2 M S SYNOPSIS S IH0 N AA1 P S IH0 S SYNOPTIC S IH0 N AA1 P T IH0 K SYNOPTICS S IH0 N AA1 P T IH0 K S SYNOVUS S AH0 N OW1 V AH0 S SYNOVUS(1) S AY2 N OW1 V AH0 S +SYNTACTIC S IH2 N T AE1 K T IH0 K SYNTAX S IH1 N T AE2 K S SYNTECH S IH1 N T EH2 K SYNTEX S IH1 N T EH2 K S @@ -117935,6 +118514,7 @@ SYSTEMONE S IH1 S T AH0 M OW2 N SYSTEMS S IH1 S T AH0 M Z SYSTEMS' S IH1 S T AH0 M Z SYSTEMWIDE S IH1 S T AH0 M W AY2 D +SYSTOLIC S IH0 S T AO1 L IH0 K SYSTRAN S AY1 S T R AE2 N SYSTRAN(1) S IH1 S T R AE2 N SYTSMA S IH1 T S M AH0 @@ -118049,6 +118629,7 @@ TABULATIONS T AE2 B Y AH0 L EY1 SH AH0 N Z TABULATURE T AE1 B Y AH0 L AH0 CH ER0 TAC T AE1 K TACEY T EY1 S IY0 +TACHYCARDIA T AA2 K IY0 K AA1 R D IY2 AH0 TACIT T AE1 S IH0 T TACITA T AA0 CH IY1 T AH0 TACITLY T AE1 S IH0 T L IY0 @@ -118409,6 +118990,7 @@ TANEY T EY1 N IY0 TANG T AE1 NG TANG(1) T AA1 NG TANGE T AE1 N JH +TANGELO T AE1 N JH EH2 L OW0 TANGEMAN T EY1 N JH M AH0 N TANGEN T AE1 NG AH0 N TANGENT T AE1 N JH AH0 N T @@ -118803,6 +119385,8 @@ TAXING T AE1 K S IH0 NG TAXIS T AE1 K S IY0 Z TAXIWAY T AE1 K S IY0 W EY0 TAXOL T AE1 K S AA2 L +TAXONOMIES T AE2 K S AO1 N AH0 M IY2 Z +TAXONOMY T AE2 K S AO1 N AH0 M IY2 TAXPAYER T AE1 K S P EY2 ER0 TAXPAYER'S T AE1 K S P EY2 ER0 Z TAXPAYERS T AE1 K S P EY2 ER0 Z @@ -119139,6 +119723,7 @@ TELEMETRY T AH0 L EH1 M AH0 T R IY0 TELEMUNDO T EH2 L AH0 M UW1 N D OW0 TELENET T EH1 L AH0 N EH2 T TELEOLOGICAL T IY2 L IY0 AH0 L AO1 JH IH0 K AH0 L +TELEOLOGY T IY1 L IY0 AO2 L AH0 JH IY2 TELEPATH T EH2 L AH0 P AE1 TH TELEPATHIC T EH2 L AH0 P AE1 TH AH0 K TELEPATHY T AH0 L EH1 P AH0 TH IY0 @@ -119286,6 +119871,7 @@ TEMPLAR T EH1 M P L ER0 TEMPLARS T EH1 M P L ER0 Z TEMPLATE T EH1 M P L AH0 T TEMPLATE(1) T EH1 M P L EY0 T +TEMPLATES T EH1 M P L AH0 T S TEMPLE T EH1 M P AH0 L TEMPLE'S T EH1 M P AH0 L Z TEMPLEMAN T EH1 M P AH0 L M AH0 N @@ -119402,6 +119988,8 @@ TENSILE T EH1 N S AH0 L TENSIOMETER T EH2 N S IY0 AA1 M IH0 T ER0 TENSION T EH1 N SH AH0 N TENSIONS T EH1 N CH AH0 N Z +TENSOR T EH1 N S ER0 +TENSORS T EH1 N S ER0 Z TENT T EH1 N T TENTACLE T EH1 N T AH0 K AH0 L TENTACLES T EH1 N T AH0 K AH0 L Z @@ -119487,6 +120075,7 @@ TERMINE T ER1 M IH0 N TERMING T ER1 M IH0 NG TERMINI T ER1 M IH0 N AY2 TERMINOLOGY T ER2 M IH0 N AA1 L AH0 JH IY0 +TERMINUS T ER1 M IH0 N AH0 S TERMITE T ER1 M AY0 T TERMITES T ER1 M AY0 T S TERMS T ER1 M Z @@ -119777,6 +120366,7 @@ THANK'S TH AE1 NG K S THANKED TH AE1 NG K T THANKFUL TH AE1 NG K F AH0 L THANKFULLY TH AE1 NG K F AH0 L IY0 +THANKFULNESS TH AE1 NG K F AH0 L N EH0 S THANKING TH AE1 NG K IH0 NG THANKLESS TH AE1 NG K L AH0 S THANKS TH AE1 NG K S @@ -119909,6 +120499,7 @@ THEOPHANIA TH IY0 AH0 F AE1 N IY0 AH0 THEOPHILA TH EY0 AH0 F IY1 L AH0 THEORA TH IY1 ER0 AH0 THEOREM TH IH1 R AH0 M +THEOREMS TH IH1 R AH0 M Z THEORETICAL TH IY2 ER0 EH1 T IH0 K AH0 L THEORETICALLY TH IY2 ER0 EH1 T IH0 K AH0 L IY0 THEORETICALLY(1) TH IY2 ER0 EH1 T IH0 K L IY0 @@ -119959,6 +120550,7 @@ THERESA(1) T ER0 EY1 S AH0 THERESA'S T ER0 IY1 S AH0 Z THERESA'S(1) T ER0 EY1 S AH0 Z THERESE TH EH1 R IY0 S +THERETO DH EH1 R T UW2 THEREUPON DH EH2 R AH0 P AA1 N THERIAULT TH EH2 R IY0 OW1 THERIEN TH IH1 R IY0 N @@ -119970,6 +120562,8 @@ THERMCO TH ER1 M K OW0 THERMEDICS TH ER0 M EH1 D IH0 K S THERMITS TH ER1 M IH0 T S THERMO TH ER1 M OW0 +THERMODYNAMIC TH ER2 M OW0 D AY2 N AE1 M IH0 K +THERMODYNAMICS TH ER2 M OW0 D AY2 N AE1 M IH0 K S THERMOMETER TH ER0 M AA1 M AH0 T ER0 THERMOMETERS TH ER0 M AA1 M AH0 T ER0 Z THERMONUCLEAR TH ER2 M OW0 N UW1 K L IY0 ER0 @@ -119985,6 +120579,7 @@ THEROUX TH ER0 UW1 THERRELL TH EH1 R AH0 L THERRIAULT TH EH1 R IY0 OW1 THERRIEN TH EH1 R IY0 N +THESAURI TH AH0 S AO1 R IY2 THESAURUS TH AH0 S AO1 R AH0 S THESE DH IY1 Z THESES TH IY1 S IY0 Z @@ -120053,6 +120648,7 @@ THIERY TH IH1 R IY0 THIES TH IY1 Z THIESEN TH IY1 S AH0 N THIESSEN TH IY1 S AH0 N +THIEVE TH IY1 V THIEVERY TH IY1 V ER0 IY0 THIEVES TH IY1 V Z THIEVES' TH IY1 V Z @@ -120124,6 +120720,7 @@ THIS'LL(1) DH IH0 S AH0 L THISSEN TH IH1 S AH0 N THISTLE TH IH1 S AH0 L THISTLES TH IH1 S AH0 L Z +THITHER DH IH1 TH ER2 THIVIERGE TH IH0 V Y EH1 R ZH THO DH OW1 THOBE TH OW1 B @@ -120644,6 +121241,7 @@ TIKI T IY1 K IY1 TIL T IH1 L TILBURY T IH1 L B EH2 R IY0 TILDA T IH1 L D AH0 +TILDE T IH1 L D AH0 TILDEN T IH1 L D AH0 N TILE T AY1 L TILED T AY1 L D @@ -120807,6 +121405,7 @@ TINMAN T IH1 N M AE2 N TINNELL T IH1 N AH0 L TINNEY T IH1 N IY0 TINNIN T IH1 N IH0 N +TINNITUS T IH1 N IH0 T AH0 S TINNON T IH1 N AH0 N TINNY T IH1 N IY0 TINO T IY1 N OW0 @@ -120946,6 +121545,7 @@ TJARKS JH AA1 R K S TKACH K AE1 CH TKACZ K AA1 CH TLATELOCO T L AE2 T IH0 L OW1 K OW0 +TLC T IY2 EH2 L S IY1 TLINGIT T L IY1 NG G IH0 T TO T UW1 TO(1) T IH0 @@ -121129,6 +121729,7 @@ TOLSMA T OW1 L S M AH0 TOLSON T OW1 L S AH0 N TOLSTOY T OW1 L S T OY2 TOLSTOY'S T OW1 L S T OY2 Z +TOLUENE T AO1 L UW0 IY2 N TOM T AA1 M TOM'S T AA1 M Z TOMA T OW1 M AH0 @@ -121700,6 +122301,7 @@ TOWERS T AW1 ER0 Z TOWERS' T AW1 ER0 Z TOWERY T OW0 ER1 IY0 TOWEY T OW1 IY0 +TOWHEE T OW1 HH IY2 TOWING T OW1 IH0 NG TOWLE T AW1 L TOWLE'S T AW1 L Z @@ -121746,6 +122348,7 @@ TOY T OY1 TOY'S T OY1 Z TOYA T OY1 AH0 TOYAMA T OW0 Y AA1 M AH0 +TOYBOY T OY1 B OY2 TOYE T OY1 TOYED T OY1 D TOYING T OY1 IH0 NG @@ -122619,8 +123222,11 @@ TRIPPETT T R IH1 P IH0 T TRIPPIE T R IH1 P IY0 TRIPPING T R IH1 P IH0 NG TRIPPLE T R IH1 P AH0 L +TRIPPY T R IH1 P IY2 TRIPS T R IH1 P S TRIPTYCH T R IH1 P T IH0 K +TRIPTYCHS T R IH1 P T IH0 K S +TRIREME T R AY1 R IY2 M TRIREMES T R AY1 R IY2 M Z TRISH T R IH1 SH TRISHA T R IH1 SH AH0 @@ -122765,7 +123371,10 @@ TROUBLEFIELD T R AH1 B AH0 L F IY2 L D TROUBLEMAKER T R AH1 B AH0 L M EY2 K ER0 TROUBLEMAKERS T R AH1 B AH0 L M EY2 K ER0 Z TROUBLES T R AH1 B AH0 L Z +TROUBLESHOOT T R AH1 B AH0 L SH UW2 T TROUBLESHOOTER T R AH1 B AH0 L SH UW2 T ER0 +TROUBLESHOOTING T R AH1 B AH0 L SH UW2 T IH0 NG +TROUBLESHOOTS T R AH1 B AH0 L SH UW2 T S TROUBLESOME T R AH1 B AH0 L S AH0 M TROUBLING T R AH1 B AH0 L IH0 NG TROUBLING(1) T R AH1 B L IH0 NG @@ -123029,9 +123638,11 @@ TUBBS T AH1 B Z TUBBY T AH1 B IY0 TUBE T UW1 B TUBE(1) T Y UW1 B +TUBER T UW1 B ER0 TUBERCULOSIS T AH0 B ER2 K Y AH0 L OW1 S IH0 S TUBERCULOSIS(1) T UW0 B ER2 K Y AH0 L OW1 S AH0 S TUBERCULOSIS(2) T UW0 B ER2 K Y UW0 L OW1 S AH0 S +TUBERS T UW1 B ER0 S TUBERVILLE T UW1 B ER0 V IH2 L TUBES T UW1 B Z TUBING T UW1 B IH0 NG @@ -123220,6 +123831,7 @@ TURBEN T ER1 B AH0 N TURBERVILLE T ER1 B ER0 V IH2 L TURBETT T ER1 B IH0 T TURBEVILLE T ER1 B V IH0 L +TURBID T ER1 B IH0 D TURBIDITY T ER0 B IH1 D AH0 T IY0 TURBIN T ER1 B IH0 N TURBINE T ER1 B AY0 N @@ -123280,6 +123892,7 @@ TURLOUGH T ER1 L OW0 TURLOUGH(1) T ER1 L AW0 TURMAN T ER1 M AH0 N TURMEL T ER1 M AH0 L +TURMERIC T ER1 M AH0 R IH2 K TURMOIL T ER1 M OY2 L TURN T ER1 N TURN-OUT T ER1 N AW2 T @@ -123415,6 +124028,8 @@ TWARDOWSKI T W ER0 D AW1 S K IY0 TWARDY T W AO1 R D IY0 TWAROG T W AO1 R AO0 G TWAS T W AH1 Z +TWAT T W AA1 T +TWATS T W AA1 T S TWEAK T W IY1 K TWEAKED T W IY1 K T TWEAKING T W IY1 K IH0 NG @@ -123469,6 +124084,7 @@ TWINGE T W IH1 N JH TWINING T W AY1 N IH0 NG TWINJET T W IH1 N JH EH2 T TWINJETS T W IH1 N JH EH2 T S +TWINK T W IH1 NG K TWINKIE T W IH1 NG K IY0 TWINKIES T W IH1 NG K IY0 Z TWINKLE T W IH1 NG K AH0 L @@ -123500,6 +124116,7 @@ TWITCHED T W IH1 CH T TWITCHELL T W IH1 CH AH0 L TWITCHES T W IH1 CH IH0 Z TWITCHING T W IH1 CH IH0 NG +TWITCHY T W IH1 CH IY2 TWITE T W AY1 T TWITTER T W IH1 T ER0 TWITTY T W IH1 T IY0 @@ -123589,6 +124206,8 @@ TYPISTS T AY1 P IH0 S T S TYPO T AY1 P OW0 TYPOGRAPHICAL T AY2 P AH0 G R AE1 F IH0 K AH0 L TYPOGRAPHY T AH0 P AA1 G R AH0 F IY0 +TYPOLOGIES T AY2 P OW1 L AH0 G IH2 Z +TYPOLOGY T AY2 P OW1 L AH0 G IH2 TYRA T AY1 R AH0 TYRANNICAL T ER0 AE1 N IH0 K AH0 L TYRANNIES T IH1 R AH0 N IY0 Z @@ -123943,6 +124562,8 @@ UNBOUND AH0 N B AW1 N D UNBOUNDED AH0 N B AW1 N D IH0 D UNBOWED AH0 N B OW1 D UNBOWED(1) AH0 N B AW1 D +UNBOX AH0 N B AO1 K S +UNBOXING AH0 N B AO1 K S IH2 NG UNBRANDED AH0 N B R AE1 N D IH0 D UNBREAKABILITY AH0 N B R EY2 K AH0 B IH1 L IH0 T IY0 UNBREAKABLE AH0 N B R EY1 K AH0 B AH0 L @@ -124906,6 +125527,7 @@ UNSTATED AH0 N S T EY1 T IH0 D UNSTEADY AH0 N S T EH1 D IY0 UNSTINTING AH0 N S T IH1 N T IH0 NG UNSTOPPABLE AH0 N S T AA1 P AH0 B AH0 L +UNSTRESSED AH0 N S T R EH1 S T UNSTRUCTURED AH0 N S T R AH1 K SH ER0 D UNSTUCK AH0 N S T AH1 K UNSUBSCRIBE AH0 N S AH0 B S K R AY1 B @@ -125242,6 +125864,8 @@ URINE Y ER1 AH0 N URINE(1) Y UH1 R AH0 N URIOSTE Y ER0 IY0 OW1 S T IY0 URKEL ER1 K AH0 L +URL Y UW2 AA2 R EH1 L +URL(1) UH1 R L URMAN ER1 M AH0 N URN ER1 N URNESS ER1 N AH0 S @@ -125293,6 +125917,7 @@ USAIR'S Y UW1 EH1 S EH1 R Z USAIRWAYS Y UW1 EH1 S EH1 R W EY2 Z USAMERIBANCS Y UW1 EH1 S AH0 M EH1 R IH0 B AE2 N K S USBANCORP Y UW1 EH1 S B AE1 NG K AO2 R P +USDA Y UW2 EH2 S D IY2 EY1 USE Y UW1 S USE(1) Y UW1 Z USEC Y UW1 S EH0 K @@ -126342,6 +126967,7 @@ VELTRE V EH1 L T ER0 VELTRI V EH1 L T R IY0 VELVEETA V EH0 L V IY1 T AH0 VELVET V EH1 L V AH0 T +VELVETEEN V EH1 L V AH0 T IY2 N VELVETY V EH1 L V AH0 T IY0 VEMICH V EH1 M IH0 CH VEMPALA V EH2 M P AA1 L AH0 @@ -126433,6 +127059,8 @@ VENTRELLA V EH2 N T R EH1 L AH0 VENTRES V EH1 N T ER0 Z VENTRESCA V EH0 N T R EH1 S K AH0 VENTRESS V EH1 N T R IH0 S +VENTRICLE V EH1 N T R IH2 K AH0 L +VENTRICLES V EH1 N T R IH2 K AH0 L Z VENTRICULAR V EH0 N T R IH1 K Y UW0 L ER0 VENTRITEX V EH1 N T R IH0 T EH2 K S VENTS V EH1 N T S @@ -126684,7 +127312,9 @@ VESTAL V EH1 S T AH0 L VESTAR V EH1 S T ER0 VESTED V EH1 S T IH0 D VESTER V EH1 S T ER0 +VESTIBULAR V EH2 S T IH1 B Y UW0 L ER0 VESTIBULE V EH1 S T IH0 B Y UW2 L +VESTIBULES V EH1 S T IH0 B Y UW2 L Z VESTIGE V EH1 S T IH0 JH VESTIGES V EH1 S T IH0 JH IH0 Z VESTIGIAL V AH0 S T IH1 JH IY0 AH0 L @@ -126732,6 +127362,7 @@ VEVILA V EY0 V IY1 L AH0 VEX V EH1 K S VEXATIOUS V EH0 K S EY1 SH AH0 S VEXED V EH1 K S T +VEXES V EH1 K S IH0 Z VEXING V EH1 K S IH0 NG VEY V EY1 VEYNE V EY1 N @@ -126774,9 +127405,11 @@ VIBRATE V AY1 B R EY0 T VIBRATES V AY1 B R EY0 T S VIBRATING V AY1 B R EY0 T IH0 NG VIBRATION V AY0 B R EY1 SH AH0 N +VIBRATIONAL V AY0 B R EY1 SH AH0 N AH0 L VIBRATIONS V AY0 B R EY1 SH AH0 N Z VIBRATO V IY0 B R AA1 T OW0 VIBRATOR V AY1 B R EY0 T ER0 +VIBRATORS V AY1 B R EY0 T ER0 Z VIC V IH1 K VIC'S V IH1 K S VICAR V IH1 K ER0 @@ -127164,6 +127797,7 @@ VIRAG V IH1 R AH0 G VIRAGO V IH0 R AA1 G OW2 VIRAL V AY1 R AH0 L VIRAMONTES V IH0 R AA0 M OW1 N T EH0 S +VIRAMUNE V IY2 R AH0 M UW1 N EH2 VIRAMUNES V IY2 R AH0 M UW1 N EH2 Z VIRATEK V IH1 R AH0 T EH2 K VIRAY V AY1 R EY0 @@ -127189,6 +127823,8 @@ VIRGINIANS V ER0 JH IH1 N Y AH0 N Z VIRGINITY V ER0 JH IH1 N IH0 T IY0 VIRGINS V ER1 JH AH0 N Z VIRGO V ER1 G OW0 +VIRGULE V IH1 R G Y UW2 L +VIRGULES V IH1 R G Y UW2 L Z VIRGY V ER1 JH IY0 VIRIDIS V IH1 R IH0 D IH2 S VIRILE V IH1 R AH0 L @@ -127307,6 +127943,7 @@ VITEZ V IH1 T EH0 Z VITEZ(1) V AY1 T EH0 Z VITI V IY1 T IY0 VITIA V IY1 SH AH0 +VITIATE V IY1 SH IH0 EY2 T VITIELLO V IY0 T IY0 EH1 L OW0 VITILIGO V IY0 T IH1 L IH0 G OW0 VITNER V IH1 T N ER0 @@ -127708,6 +128345,7 @@ VROMAN V R OW1 M AH0 N VROOM V R UW1 M VROOMAN V R UW1 M AH0 N VS V ER1 S AH0 Z +VS. V ER1 S AH0 Z VSEL V IY1 S EH2 L VU V UW1 VUE V Y UW1 @@ -127806,6 +128444,7 @@ WADERS W EY1 D ER0 Z WADES W EY1 D Z WADFORD W AO1 D F ER0 D WADHAMS W AO1 D AH0 M Z +WADI W AA1 D IY2 WADING W EY1 D IH0 NG WADKINS W AO1 D K IH0 N Z WADLE W AO1 D AH0 L @@ -127919,6 +128558,7 @@ WAINMAN W EY1 N M AH0 N WAINOCO W EY2 N OW1 K OW0 WAINOCO'S W EY2 N OW1 K OW0 Z WAINRIGHT W EY1 N R AY2 T +WAINSCOT W EY1 N S K AH0 T WAINSCOTT W EY1 N S K AH0 T WAINSCOTTING W EY1 N S K AO0 T IH0 NG WAINWRIGHT W EY1 N R AY2 T @@ -128087,6 +128727,7 @@ WALKWAY W AO1 K W EY2 WALKWAYS W AO1 K W EY2 Z WALL W AO1 L WALL'S W AO1 L Z +WALL-E W AO1 L IY2 WALL-TEX W AO1 L T EH1 K S WALLA W AO1 L AH0 WALLABIES W AA1 L AH0 B IY0 Z @@ -128252,6 +128893,8 @@ WANGLER W AE1 NG G L ER0 WANING W EY1 N IH0 NG WANK W AA1 NG K WANKE W AA1 NG K +WANKER W AA1 NG K ER0 +WANKERS W AA1 NG K ER0 Z WANKO W AA1 NG K OW0 WANLESS W AA1 N L AH0 S WANN W AA1 N @@ -128340,6 +128983,7 @@ WAREHOUSING W EH1 R HH AW2 Z IH0 NG WAREING W EH1 R IH0 NG WAREN W EH1 R AH0 N WARES W EH1 R Z +WAREZ W EH1 R Z WARF W AO1 R F WARFARE W AO1 R F EH2 R WARFEL W AO1 R F AH0 L @@ -128682,6 +129326,8 @@ WAUSAU W AO1 S AO0 WAUTERS W AW1 T ER0 Z WAVE W EY1 V WAVED W EY1 V D +WAVEFORM W EY1 V F AO2 R M +WAVEFORMS W EY1 V F AO2 R M Z WAVELENGTH W EY1 V L EH2 NG TH WAVELENGTHS W EY1 V L EH2 NG TH S WAVER W EY1 V ER0 @@ -128832,6 +129478,7 @@ WEBERG W EH1 B ER0 G WEBERS W EH1 B ER0 Z WEBLEY W EH1 B L IY0 WEBMASTER W EH1 B M AE2 S T ER0 +WEBMASTERS W EH1 B M AE2 S T ER0 Z WEBRE W EH1 B ER0 WEBS W EH1 B Z WEBSITE W EH1 B S AY2 T @@ -129771,6 +130418,7 @@ WHEREAS W EH0 R AE1 Z WHEREAS(1) HH W EH0 R AE1 Z WHEREBY W EH0 R B AY1 WHEREBY(1) HH W EH0 R B AY1 +WHEREFORE HH W EH1 R F AO2 R WHEREIN W EH0 R IH1 N WHEREIN(1) HH W EH0 R IH1 N WHEREUPON W EH1 R AH0 P AA1 N @@ -130522,6 +131170,8 @@ WIGGINGTON W IH1 G IH0 NG T AH0 N WIGGINS W IH1 G IH0 N Z WIGGINTON W IH1 G IH0 N T AH0 N WIGGLE W IH1 G AH0 L +WIGGLED W IH1 G AH0 L D +WIGGLES W IH1 G AH0 L Z WIGGLESWORTH W IH1 G AH0 L Z W ER2 TH WIGGLING W IH1 G AH0 L IH0 NG WIGGLING(1) W IH1 G L IH0 NG @@ -131051,6 +131701,7 @@ WINTER'S W IH1 N T ER0 Z WINTERBERG W IH1 N T ER0 B ER0 G WINTERBOURNE W IH1 N T ER0 B AO2 R N WINTERED W IH1 N T ER0 D +WINTERGREEN W IH1 N T ER0 G R IY2 N WINTERHALTER W IH1 N T ER0 HH AO2 L T ER0 WINTERIZE W IH1 N T ER0 AY2 Z WINTERIZED W IH1 N T ER0 AY2 Z D @@ -131092,6 +131743,8 @@ WIPING W AY1 P IH0 NG WIPPERFURTH W IH1 P ER0 F ER0 TH WIRE W AY1 ER0 WIRE(1) W AY1 R +WIRED W AY1 ER0 D +WIRED(1) W AY1 R D WIRELESS W AY1 R L IH0 S WIRELESS'S W AY1 ER0 L AH0 S IH0 Z WIRELINE W AY1 R L AY2 N @@ -131703,6 +132356,8 @@ WORKADAY W ER1 K AH0 D EY2 WORKAHOLIC W ER1 K AH0 HH AA1 L IH0 K WORKAHOLICS W ER2 K AH0 HH AA1 L IH0 K S WORKBENCH W ER1 K B EH2 N CH +WORKBOOK W ER1 K B UH2 K +WORKBOOKS W ER1 K B UH2 K S WORKDAY W ER1 K D EY2 WORKDAYS W ER1 K D EY2 Z WORKED W ER1 K T @@ -131761,6 +132416,7 @@ WORLDPASS W ER1 L D P AE2 S WORLDS W ER1 L D Z WORLDSCOPE W ER1 L D S K OW2 P WORLDSPAN W ER1 L D S P AE2 N +WORLDVIEW W ER1 L D V Y UW2 WORLDVISION W ER1 L D V IH2 ZH AH0 N WORLDWIDE W ER1 L D W AY1 D WORLDWIDE'S W ER1 L D W AY2 D Z @@ -131968,6 +132624,7 @@ WRITERS R AY1 T ER0 Z WRITERS' R AY1 T ER0 Z WRITES R AY1 T S WRITHE R IH1 TH +WRITHED R IH1 TH D WRITHING R AY1 DH IH0 NG WRITHING(1) R IH1 TH IH0 NG WRITING R AY1 T IH0 NG @@ -132107,6 +132764,7 @@ WYNNONA W AY0 N OW1 N AH0 WYNNS W IH1 N Z WYNONA W AY0 N OW1 N AH0 WYNONA(1) HH W AY0 N OW1 N AH0 +WYNONNA W AY0 N OW1 N AH0 WYNTER W IH1 N T ER0 WYNTON W IH1 N T AH0 N WYNYARD W IH1 N Y ER0 D @@ -132117,6 +132775,7 @@ WYRICK W IH1 R IH0 K WYSE W AY1 Z WYSE'S W AY1 Z IH0 Z WYSER W AY1 Z ER0 +WYSIWIG W IH1 Z IY2 W IH2 G WYSOCKI V IH0 S OW1 T S K IY0 WYSOCKI(1) V IH0 S AA1 T S K IY0 WYSONG W IH1 S AO0 NG @@ -132135,6 +132794,7 @@ X.ERS EH1 K S ER0 Z X.S EH1 K S IH0 Z XAN SH AA1 N XANADA Z AH0 N AA1 D AH0 +XANADU Z AA1 N AH0 D UW2 XANAX Z AE1 N AE0 K S XANTHE Z AE1 N DH XANTIPPE Z AE1 N T IH0 P @@ -132208,6 +132868,7 @@ Y'KNOW Y AH0 N OW1 Y'S W AY1 Z Y. W AY1 Y.'S W AY1 Z +Y2K W AY2 T UW2 K EY1 YA Y AA1 YA'LL Y AA1 L YAACOV Y AA1 K OW2 V @@ -132525,6 +133186,7 @@ YELLOWSTONE Y EH1 L OW0 S T OW2 N YELLOWSTONE'S Y EH1 L OW0 S T OW2 N Z YELLS Y EH1 L Z YELP Y EH1 L P +YELPED Y EH1 L P D YELPING Y EH1 L P IH0 NG YELTON Y EH1 L T AH0 N YELTSIN Y EH1 L T S AH0 N @@ -132619,6 +133281,7 @@ YINGLING(1) Y IH1 NG G L IH0 NG YINGST Y IH1 NG G S T YINGST(1) Y IH1 NG K S T YIP Y IH1 P +YIPEE Y IH2 P IY1 YIPPEE Y IH2 P IY1 YIRNG-AN Y IH1 R NG AA1 N YITZHAK Y IH1 T S AA0 K @@ -132688,6 +133351,7 @@ YOLANDA Y OW0 L AA1 N D AH0 YOLANDE Y OW1 L AH0 N D YOLK Y OW1 K YOLKS Y OW1 K S +YOLO Y OW1 L OW2 YOM Y AA1 M YOM(1) Y OW1 M YOM'S Y AA1 M Z @@ -133651,6 +134315,7 @@ ZOGBY Z AO1 G B IY0 ZOGG Z AA1 G ZOGHBY Z OW1 B IY0 ZOH Z OW1 +ZOHN Z OW1 N ZOLA Z OW1 L AH0 ZOLL Z AA1 L ZOLLARS Z AA1 L ER0 Z @@ -134018,7 +134683,6 @@ SUPERPOD S UW2 P ER0 P AO1 D SUPERRES S UW2 P ER0 R R AE1 S TABULAR T AE1 B Y AH0 L AA1 R TCO T IY1 S IY1 OW1 -TENSOR T EH1 N S ER0 TENSORRT T EH1 N S ER0 AA1 R T IY1 TERA T EH1 R AH0 TERAFLOP T EH1 R AH0 F L AA2 P @@ -134112,4 +134776,3 @@ TUESDAY(1) T UW1 Z D IY0 TV T IY1 V IY1 WEDNESDAY W EH1 N Z D EY2 WEDNESDAY(1) W EH1 N Z D IY0 -WIRED W AY1 ER0 D diff --git a/scripts/tts_dataset_files/heteronyms-030921 b/scripts/tts_dataset_files/heteronyms-030921 deleted file mode 100644 index 1706465b7995..000000000000 --- a/scripts/tts_dataset_files/heteronyms-030921 +++ /dev/null @@ -1,413 +0,0 @@ -abject -abrogate -absent -abstract -abuse -ache -acre -acuminate -addict -address -adduct -adele -advocate -affect -affiliate -agape -aged -agglomerate -aggregate -agonic -agora -allied -ally -alternate -alum -am -analyses -andrea -animate -apply -appropriate -approximate -ares -arithmetic -arsenic -articulate -associate -attribute -august -axes -ay -aye -bases -bass -bathed -bested -bifurcate -blessed -blotto -bow -bowed -bowman -brassy -buffet -bustier -carbonate -celtic -choral -chumash -close -closer -coax -coincidence -color coordinate -colour coordinate -comber -combine -combs -committee -commune -compact -complex -compound -compress -concert -conduct -confine -confines -conflict -conglomerate -conscript -conserve -consist -console -consort -construct -consult -consummate -content -contest -contract -contracts -contrast -converse -convert -convict -coop -coordinate -covey -crooked -curate -cussed -decollate -decrease -defect -defense -delegate -deliberate -denier -desert -detail -deviate -diagnoses -diffuse -digest -discard -discharge -discount -do -document -does -dogged -domesticate -dominican -dove -dr -drawer -duplicate -egress -ejaculate -eject -elaborate -ellipses -email -emu -entrace -entrance -escort -estimate -eta -etna -evening -excise -excuse -exploit -export -extract -fine -flower -forbear -four-legged -frequent -furrier -gallant -gel -geminate -gillie -glower -gotham -graduate -haggis -heavy -hinder -house -housewife -impact -imped -implant -implement -import -impress -incense -incline -increase -infix -insert -instar -insult -integral -intercept -interchange -interflow -interleaf -intermediate -intern -interspace -intimate -intrigue -invalid -invert -invite -irony -jagged -jesses -julies -kite -laminate -laos -lather -lead -learned -leasing -lech -legitimate -lied -lima -lipread -live -lower -lunged -maas -magdalen -manes -mare -marked -merchandise -merlion -minute -misconduct -misled -misprint -mobile -moderate -mong -moped -moth -mouth -mow -mpg -multiply -mush -nana -nice -nice -number -numerate -nun -object -opiate -ornament -outbox -outcry -outpour -outreach -outride -outright -outside -outwork -overall -overbid -overcall -overcast -overfall -overflow -overhaul -overhead -overlap -overlay -overuse -overweight -overwork -pace -palled -palling -para -pasty -pate -pauline -pedal -peer -perfect -periodic -permit -pervert -pinta -placer -platy -polish -polish -poll -pontificate -postulate -pram -prayer -precipitate -predate -predicate -prefix -preposition -present -pretest -primer -proceeds -produce -progress -project -proportionate -prospect -protest -pussy -putter -putting -quite -ragged -raven -re -read -reading -reading -real -rebel -recall -recap -recitative -recollect -record -recreate -recreation -redress -refill -refund -refuse -reject -relay -remake -repaint -reprint -reread -rerun -resent -reside -resign -respray -resume -retard -retest -retread -rewrite -root -routed -routing -row -rugged -rummy -sais -sake -sambuca -saucier -second -secrete -secreted -secreting -segment -separate -sewer -shirk -shower -sin -skied -slaver -slough -sow -spoof -squid -stingy -subject -subordinate -subvert -supply -supposed -survey -suspect -syringes -tabulate -tales -tarrier -tarry -taxes -taxis -tear -theron -thou -three-legged -tier -tinged -torment -transfer -transform -transplant -transport -transpose -tush -two-legged -unionised -unionized -update -uplift -upset -use -used -vale -violist -viva -ware -whinged -whoop -wicked -wind -windy -wino -won -worsted -wound diff --git a/scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.06.txt b/scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.08.txt similarity index 99% rename from scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.06.txt rename to scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.08.txt index 95ef23f8800d..2f32c76f095c 100644 --- a/scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.06.txt +++ b/scripts/tts_dataset_files/ipa_cmudict-0.7b_nv22.08.txt @@ -57581,7 +57581,7 @@ HURRAY həˈɹeɪ HURRELL ˈhɔɹəɫ HURRI ˈhɝi HURRICANE ˈhɝəˌkeɪn -HURRICANE ˈhəɹəˌkeɪnz +HURRICANE ˈhəɹəˌkeɪn HURRICANE'S ˈhɝəˌkeɪnz HURRICANES ˈhɝəˌkeɪnz HURRIED ˈhɝid @@ -77809,7 +77809,8 @@ MERCEDESES ˌmɝˈseɪˌdiz MERCENARIES ˈmɝsəˌnɛɹiz MERCENARY ˈmɝsəˌnɛɹi MERCER ˈmɝsɝ -MERCHANDISE ˈmɝtʃənˌdaɪz +MERCHANDISE ˈmɝtʃənˌdaɪs +MERCHANDISE(1) ˈmɝtʃənˌdaɪz MERCHANDISER ˈmɝtʃənˌdaɪzɝ MERCHANDISERS ˈmɝtʃənˌdaɪzɝz MERCHANDISING ˈmɝtʃənˌdaɪzɪŋ diff --git a/tests/collections/nlp/test_prompt_learning.py b/tests/collections/nlp/test_prompt_learning.py index f7d25d41fcf0..ed0a227cbda3 100644 --- a/tests/collections/nlp/test_prompt_learning.py +++ b/tests/collections/nlp/test_prompt_learning.py @@ -29,7 +29,7 @@ def get_prompt_tuning_dataset( dataset_path, tokenizer, virtual_prompt_source, task_templates, pseudo_tokens, ): dataset = GPTPromptLearningDataset( - dataset_paths=[dataset_path], + data=[dataset_path], tokenizer=tokenizer, virtual_prompt_source=virtual_prompt_source, task_templates=task_templates, diff --git a/tutorials/asr/Buffered_Transducer_Inference.ipynb b/tutorials/asr/Buffered_Transducer_Inference.ipynb index 41128549a7bf..c1d922a13010 100644 --- a/tutorials/asr/Buffered_Transducer_Inference.ipynb +++ b/tutorials/asr/Buffered_Transducer_Inference.ipynb @@ -833,7 +833,7 @@ "\n", " for t in range(len(alignments)):\n", " for u in range(len(alignments[t])):\n", - " token_id = int(alignments[t][u])\n", + " token_id = int(alignments[t][u][1])\n", " if token_id != blank_id:\n", " token = tokenizer.ids_to_tokens([token_id])[0]\n", " s.append(token)\n", @@ -1596,7 +1596,7 @@ "for ti in range(len(alignments)):\n", " t_u = []\n", " for uj in range(len(alignments[ti])):\n", - " token = alignments[ti][uj]\n", + " logprobs, token = alignments[ti][uj]\n", " token = token.to('cpu').numpy().tolist()\n", " decoded_token = model.decoding.decode_ids_to_tokens([token])[0] if token != model.decoding.blank_id else '' # token at index len(vocab) == RNNT blank token\n", " t_u.append(decoded_token)\n", diff --git a/tutorials/asr/Offline_ASR.ipynb b/tutorials/asr/Offline_ASR.ipynb index 0274e1ec57a9..2dd4cbe9d814 100644 --- a/tutorials/asr/Offline_ASR.ipynb +++ b/tutorials/asr/Offline_ASR.ipynb @@ -631,7 +631,7 @@ "cell_type": "code", "source": [ "# 40ms is duration of a timestep at output of the Conformer\n", - "time_stride = 4 * model.cfg.preprocessor.window_stride\n", + "time_stride = 4 * asr_model_subword.cfg.preprocessor.window_stride\n", "\n", "##################################################################\n", "\n", diff --git a/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb b/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb index 7917a5da2170..29913fe0fe73 100644 --- a/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb +++ b/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb @@ -97,7 +97,7 @@ "source": [ "input_manifest=\"chris_demo.json\"\n", "vad_out_manifest_filepath=\"vad_out.json\"\n", - "vad_model=\"vad_marblenet\" # here we use vad_marblenet for example, you can choose other VAD models." + "vad_model=\"vad_multilingual_marblenet\" # here we use vad_multilingual_marblenet for example, you can choose other VAD models." ] }, { @@ -171,9 +171,9 @@ "vad.model_path=$vad_model \\\n", "frame_out_dir=\"chris_demo\" \\\n", "vad.parameters.window_length_in_sec=0.63 \\\n", - "vad.parameters.postprocessing.onset=0.5 \\\n", - "vad.parameters.postprocessing.offset=0.5 \\\n", - "vad.parameters.postprocessing.min_duration_on=0.5 \\\n", + "vad.parameters.postprocessing.onset=0.7 \\\n", + "vad.parameters.postprocessing.offset=0.4 \\\n", + "vad.parameters.postprocessing.min_duration_on=1 \\\n", "vad.parameters.postprocessing.min_duration_off=0.5 \\\n", "out_manifest_filepath=$vad_out_manifest_filepath" ] diff --git a/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb b/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb index 26f82121410c..104d69df18e2 100644 --- a/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb +++ b/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb @@ -700,12 +700,12 @@ "run_name = \"test\"\n", "\n", "# checks if we have GPU available and uses it\n", - "cuda = 1 if torch.cuda.is_available() else 0\n", - "config.trainer.gpus = cuda\n", - "config.trainer.precision = 16 if torch.cuda.is_available() else 32\n", + "accelerator = 'gpu' if torch.cuda.is_available() else 'cpu'\n", + "config.trainer.devices = 1\n", + "config.trainer.accelerator = accelerator\n", "\n", "# remove distributed training flags\n", - "config.trainer.accelerator = None\n", + "config.trainer.strategy = None\n", "\n", "trainer = pl.Trainer(**config.trainer)\n", "config.exp_manager.exp_dir = os.path.join(DATA_DIR, \"output/\" + run_name)\n", diff --git a/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb b/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb index 4df04f97e180..3986e0d864d4 100644 --- a/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb +++ b/tutorials/nlp/Multitask_Prompt_and_PTuning.ipynb @@ -7,7 +7,7 @@ "metadata": {}, "outputs": [], "source": [ - "BRANCH=\"main\"" + "BRANCH='main'" ] }, { @@ -45,7 +45,7 @@ "\n", "- Our p-tuning implementation is based off Liu et al's paper [GPT Understands, Too](https://arxiv.org/abs/2103.10385).\n", "\n", - "- Usage examples and API documentation can be found in [our user docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/prompt_learning.html). \n", + "- Command line usage examples and API documentation can be found in [our user docs](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/prompt_learning.html). \n", "\n", "\"Prompt\n", "\n", diff --git a/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb b/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb index 07e354bd8cc4..ddfe431e1518 100644 --- a/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb +++ b/tutorials/speaker_tasks/Speaker_Diarization_Inference.ipynb @@ -376,7 +376,7 @@ "metadata": {}, "outputs": [], "source": [ - "pretrained_vad = 'vad_marblenet'\n", + "pretrained_vad = 'vad_multilingual_marblenet'\n", "pretrained_speaker_model = 'titanet_large'" ] }, @@ -384,7 +384,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note in this tutorial, we use the VAD model MarbleNet-3x2 introduced and published in [ICASSP MarbleNet](https://arxiv.org/pdf/2010.13886.pdf). You might need to tune on dev set similar to your dataset if you would like to improve the performance.\n", + "Note in this tutorial, we use the VAD model *vad_multilingual_marblenet* which is an improved model based on MarbleNet-3x2 that has been introduced and published in [ICASSP MarbleNet](https://arxiv.org/pdf/2010.13886.pdf). You might need to tune on dev set similar to your dataset if you would like to improve the performance.\n", "\n", "And the speakerNet-M-Diarization model achieves 7.3% confusion error rate on CH109 set with oracle vad. This model is trained on voxceleb1, voxceleb2, Fisher, SwitchBoard datasets. So for more improved performance specific to your dataset, finetune speaker verification model with a devset similar to your test set.\n", "\n", diff --git a/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb b/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb index 9d0ae82c3ebf..6ea05b619dcc 100644 --- a/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb +++ b/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb @@ -596,7 +596,8 @@ "metadata": { "colab": {}, "colab_type": "code", - "id": "HvYhsOWuSpL_" + "id": "HvYhsOWuSpL_", + "scrolled": false }, "outputs": [], "source": [ @@ -768,7 +769,7 @@ "metadata": {}, "outputs": [], "source": [ - "# !wget -P conf https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/speaker_tasks/recognition/conf/titanet-finetune.yaml\n", + "!wget -P conf https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/speaker_tasks/recognition/conf/titanet-finetune.yaml\n", "MODEL_CONFIG = os.path.join(NEMO_ROOT,'conf/titanet-finetune.yaml')\n", "finetune_config = OmegaConf.load(MODEL_CONFIG)\n", "print(OmegaConf.to_yaml(finetune_config))" diff --git a/tutorials/tts/Aligner_Inference_Examples.ipynb b/tutorials/tts/Aligner_Inference_Examples.ipynb index 057b39ce167d..bfc6d568e647 100644 --- a/tutorials/tts/Aligner_Inference_Examples.ipynb +++ b/tutorials/tts/Aligner_Inference_Examples.ipynb @@ -43,7 +43,9 @@ "# # If you're using Colab and not running locally, uncomment and run this cell.\n", "# !apt-get install sox libsndfile1 ffmpeg\n", "# !pip install wget unidecode\n", - "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]" + "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n", + "# !wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n", + "# !bash install_pynini.sh" ] }, { @@ -80,9 +82,7 @@ "source": [ "## Setup\n", "\n", - "Let's start by loading the checkpoint, which should have automatically been saved by the trainer as a file in the output directory's `checkpoints/` folder called `Aligner.nemo`.\n", - "\n", - "Please update `ckpt_path` in the cell below to the appropriate location on your machine." + "Let's start by loading the checkpoint from NGC. You can find the model card [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_en_radtts_aligner)." ] }, { @@ -95,10 +95,8 @@ "# Set device (GPU or CPU)\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "\n", - "# Replace the following with the path to your Aligner model checkpoint\n", - "# TODO: Update once NGC checkpoint is released\n", - "ckpt_path = \"/Aligner.nemo\"\n", - "aligner = AlignerModel.restore_from(ckpt_path, map_location=device)\n", + "# Load the ARPABET Aligner model checkpoint\n", + "aligner = AlignerModel.from_pretrained(\"tts_en_radtts_aligner\")\n", "\n", "# This should be set to whatever sample rate your model was trained on\n", "target_sr = 22050" diff --git a/tutorials/tts/FastPitch_Finetuning.ipynb b/tutorials/tts/FastPitch_Finetuning.ipynb index 19b509502ef3..34c96ebf6474 100755 --- a/tutorials/tts/FastPitch_Finetuning.ipynb +++ b/tutorials/tts/FastPitch_Finetuning.ipynb @@ -61,7 +61,9 @@ "# # If you're using Google Colab and not running locally, uncomment and run this cell.\n", "# !apt-get install sox libsndfile1 ffmpeg\n", "# !pip install wget unidecode pynini==2.1.4\n", - "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]" + "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n", + "# !wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n", + "# !bash install_pynini.sh" ] }, { @@ -244,8 +246,8 @@ "source": [ "# additional files\n", "!mkdir -p tts_dataset_files && cd tts_dataset_files \\\n", - "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/cmudict-0.7b_nv22.07 \\\n", - "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/heteronyms-030921 \\\n", + "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/cmudict-0.7b_nv22.08 \\\n", + "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/heteronyms-052722 \\\n", "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv \\\n", "&& cd .." ] @@ -286,8 +288,8 @@ " train_dataset=./6097_manifest_train_dur_5_mins_local.json \\\n", " validation_datasets=./6097_manifest_dev_ns_all_local.json \\\n", " sup_data_path=./fastpitch_sup_data \\\n", - " phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.07 \\\n", - " heteronyms_path=tts_dataset_files/heteronyms-030921 \\\n", + " phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.08 \\\n", + " heteronyms_path=tts_dataset_files/heteronyms-052722 \\\n", " whitelist_path=tts_dataset_files/lj_speech.tsv \\\n", " exp_manager.exp_dir=./ljspeech_to_6097_no_mixing_5_mins \\\n", " +init_from_nemo_model=./tts_en_fastpitch_align.nemo \\\n", @@ -318,11 +320,11 @@ " sup_data_path=./fastpitch_sup_data`\n", " * We tell the script what manifest files to train and eval on, as well as where supplementary data is located (or will be calculated and saved during training if not provided).\n", " \n", - "* `phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.07 \n", - "heteronyms_path=tts_dataset_files/heteronyms-030921\n", + "* `phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.08 \n", + "heteronyms_path=tts_dataset_files/heteronyms-052722\n", "whitelist_path=tts_dataset_files/lj_speech.tsv \n", "`\n", - " * We tell the script where `phoneme_dict_path`, `heteronyms-030921` and `whitelist_path` are located. These are the additional files we downloaded earlier, and are used in preprocessing the data.\n", + " * We tell the script where `phoneme_dict_path`, `heteronyms-052722` and `whitelist_path` are located. These are the additional files we downloaded earlier, and are used in preprocessing the data.\n", " \n", "* `exp_manager.exp_dir=./ljspeech_to_6097_no_mixing_5_mins`\n", " * Where we want to save our log files, tensorboard file, checkpoints, and more.\n", diff --git a/tutorials/tts/Fastpitch_Training_GermanTTS.ipynb b/tutorials/tts/FastPitch_GermanTTS_Training.ipynb similarity index 95% rename from tutorials/tts/Fastpitch_Training_GermanTTS.ipynb rename to tutorials/tts/FastPitch_GermanTTS_Training.ipynb index 72fb31dfaedf..4754cec9bf65 100644 --- a/tutorials/tts/Fastpitch_Training_GermanTTS.ipynb +++ b/tutorials/tts/FastPitch_GermanTTS_Training.ipynb @@ -55,7 +55,9 @@ "# # If you're using Colab and not running locally, uncomment and run this cell.\n", "# !apt-get install sox libsndfile1 ffmpeg\n", "# !pip install wget unidecode pynini==2.1.4 scipy==1.7.3\n", - "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]" + "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n", + "# !wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n", + "# !bash install_pynini.sh" ] }, { @@ -79,7 +81,11 @@ "cell_type": "code", "execution_count": null, "id": "c588ff4f", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# lets download the files we need to run this tutorial\n", @@ -93,7 +99,7 @@ "!cd NeMoGermanTTS && wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/tts/conf/hifigan/hifigan.yaml\n", "!cd NeMoGermanTTS && wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/nemo_text_processing/text_normalization/de/data/whitelist.tsv\n", "!cd NeMoGermanTTS && mkdir -p model/train_ds && cd model/train_ds && wget https://raw.githubusercontent.com/nvidia/NeMo/$BRANCH/examples/tts/conf/hifigan/model/train_ds/train_ds_finetune.yaml\n", - "!cd NeMoGermanTTS && mkdir -p model/train_ds && cd model/validation_ds && wget https://raw.githubusercontent.com/nvidia/NeMo/$BRANCH/examples/tts/conf/hifigan/model/validation_ds/val_ds_finetune.yaml\n", + "!cd NeMoGermanTTS && mkdir -p model/validation_ds && cd model/validation_ds && wget https://raw.githubusercontent.com/nvidia/NeMo/$BRANCH/examples/tts/conf/hifigan/model/validation_ds/val_ds_finetune.yaml\n", "!cd NeMoGermanTTS && mkdir -p model/generator && cd model/generator && wget https://raw.githubusercontent.com/nvidia/NeMo/$BRANCH/examples/tts/conf/hifigan/model/generator/v1.yaml" ] }, @@ -492,6 +498,67 @@ "Save the above config in `NeMoGermanTTS/ds_for_fastpitch_align.yaml`." ] }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "tmp = '''\\\n", + "name: \"ds_for_fastpitch_align\"\n", + "\n", + "manifest_filepath: \"train_manifest.json\"\n", + "sup_data_path: \"sup_data\"\n", + "sup_data_types: [ \"align_prior_matrix\", \"pitch\" ]\n", + "whitelist_path: \"NeMoGermanTTS/whitelist.tsv\"\n", + "\n", + "dataset:\n", + " _target_: nemo.collections.tts.torch.data.TTSDataset\n", + " manifest_filepath: ${manifest_filepath}\n", + " sample_rate: 22050\n", + " sup_data_path: ${sup_data_path}\n", + " sup_data_types: ${sup_data_types}\n", + " n_fft: 1024\n", + " win_length: 1024\n", + " hop_length: 256\n", + " window: \"hann\"\n", + " n_mels: 80\n", + " lowfreq: 0\n", + " highfreq: 8000\n", + " max_duration: null\n", + " min_duration: 0.1\n", + " ignore_file: null\n", + " trim: false\n", + " pitch_fmin: 65.40639132514966\n", + " pitch_fmax: 2093.004522404789\n", + "\n", + " text_normalizer:\n", + " _target_: nemo_text_processing.text_normalization.normalize.Normalizer\n", + " lang: de\n", + " input_case: cased\n", + " whitelist: ${whitelist_path}\n", + "\n", + " text_normalizer_call_kwargs:\n", + " verbose: false\n", + " punct_pre_process: true\n", + " punct_post_process: true\n", + "\n", + " text_tokenizer:\n", + " _target_: nemo.collections.tts.torch.tts_tokenizers.GermanCharsTokenizer\n", + " punct: true\n", + " apostrophe: true\n", + " pad_with_space: true\n", + " phonemes: true\n", + "'''\n", + "with open('NeMoGermanTTS/ds_for_fastpitch_align.yaml', 'w') as f:\n", + " f.write(tmp)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, { "cell_type": "markdown", "id": "b515c5b4", @@ -1225,4 +1292,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/tutorials/tts/FastPitch_MixerTTS_Training.ipynb b/tutorials/tts/FastPitch_MixerTTS_Training.ipynb index e84730a65040..627ded57d315 100644 --- a/tutorials/tts/FastPitch_MixerTTS_Training.ipynb +++ b/tutorials/tts/FastPitch_MixerTTS_Training.ipynb @@ -229,8 +229,8 @@ "\n", "# additional files\n", "!mkdir -p tts_dataset_files && cd tts_dataset_files \\\n", - "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/cmudict-0.7b_nv22.07 \\\n", - "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/heteronyms-030921 \\\n", + "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/cmudict-0.7b_nv22.08 \\\n", + "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/heteronyms-052722 \\\n", "&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv \\\n", "&& cd .." ] @@ -431,8 +431,8 @@ "\n", "# Grapheme-to-phoneme module\n", "g2p = EnglishG2p(\n", - " phoneme_dict=\"tts_dataset_files/cmudict-0.7b_nv22.07\",\n", - " heteronyms=\"tts_dataset_files/heteronyms-030921\"\n", + " phoneme_dict=\"tts_dataset_files/cmudict-0.7b_nv22.08\",\n", + " heteronyms=\"tts_dataset_files/heteronyms-052722\"\n", ")\n", "\n", "# Text tokenizer\n", @@ -557,8 +557,8 @@ "validation_datasets=tests/data/asr/an4_val.json \\\n", "sup_data_types=\"['align_prior_matrix', 'pitch']\" \\\n", "sup_data_path={mixer_tts_sup_data_path} \\\n", - "phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.07 \\\n", - "heteronyms_path=tts_dataset_files/heteronyms-030921 \\\n", + "phoneme_dict_path=tts_dataset_files/cmudict-0.7b_nv22.08 \\\n", + "heteronyms_path=tts_dataset_files/heteronyms-052722 \\\n", "whitelist_path=tts_dataset_files/lj_speech.tsv \\\n", "pitch_mean={pitch_mean} \\\n", "pitch_std={pitch_std} \\\n", diff --git a/tutorials/tts/Inference_DurationPitchControl.ipynb b/tutorials/tts/Inference_DurationPitchControl.ipynb index b7918f026171..766543d1cca2 100644 --- a/tutorials/tts/Inference_DurationPitchControl.ipynb +++ b/tutorials/tts/Inference_DurationPitchControl.ipynb @@ -50,7 +50,9 @@ "# # If you're using Google Colab and not running locally, uncomment and run this cell.\n", "# !apt-get install sox libsndfile1 ffmpeg\n", "# !pip install wget unidecode\n", - "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]" + "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n", + "# !wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n", + "# !bash install_pynini.sh" ] }, { @@ -509,4 +511,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tutorials/tts/Inference_ModelSelect.ipynb b/tutorials/tts/Inference_ModelSelect.ipynb index d2fc19e79d19..988fdd551c0c 100644 --- a/tutorials/tts/Inference_ModelSelect.ipynb +++ b/tutorials/tts/Inference_ModelSelect.ipynb @@ -50,7 +50,9 @@ "# # If you're using Google Colab and not running locally, uncomment and run this cell.\n", "# !apt-get install sox libsndfile1 ffmpeg\n", "# !pip install wget unidecode\n", - "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]" + "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n", + "# !wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n", + "# !bash install_pynini.sh" ] }, { @@ -334,4 +336,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/tutorials/tts/Tacotron2_Training.ipynb b/tutorials/tts/Tacotron2_Training.ipynb index 3602e4fec24f..8d44d49659cf 100644 --- a/tutorials/tts/Tacotron2_Training.ipynb +++ b/tutorials/tts/Tacotron2_Training.ipynb @@ -58,7 +58,9 @@ "# # If you're using Colab and not running locally, uncomment and run this cell.\n", "# !apt-get install sox libsndfile1 ffmpeg\n", "# !pip install wget unidecode\n", - "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]" + "# !python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n", + "# !wget https://raw.githubusercontent.com/NVIDIA/NeMo/main/nemo_text_processing/install_pynini.sh\n", + "# !bash install_pynini.sh" ] }, { @@ -163,8 +165,8 @@ "# We will also need a few extra files for handling text.\n", "!(mkdir -p scripts/tts_dataset_files \\\n", " && cd scripts/tts_dataset_files \\\n", - " && wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/cmudict-0.7b_nv22.07 \\\n", - " && wget wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/heteronyms-030921 \\\n", + " && wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/cmudict-0.7b_nv22.08 \\\n", + " && wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/scripts/tts_dataset_files/heteronyms-052722 \\\n", " && cd ..)\n", " \n", "!(mkdir -p nemo_text_processing/text_normalization/en/data/whitelist/ \\\n", @@ -231,8 +233,8 @@ "sup_data_path: null\n", "sup_data_types: null\n", "\n", - "phoneme_dict_path: \"scripts/tts_dataset_files/cmudict-0.7b_nv22.07\"\n", - "heteronyms_path: \"scripts/tts_dataset_files/heteronyms-030921\"\n", + "phoneme_dict_path: \"scripts/tts_dataset_files/cmudict-0.7b_nv22.08\"\n", + "heteronyms_path: \"scripts/tts_dataset_files/heteronyms-052722\"\n", "whitelist_path: \"nemo_text_processing/text_normalization/en/data/whitelist/lj_speech.tsv\"\n", "```\n", "\n",