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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,257 @@ | ||
| # Copyright 2022-2023 XProbe Inc. | ||
| # | ||
| # 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. | ||
|
|
||
| import datetime | ||
| import logging | ||
| import os | ||
| import tempfile | ||
| from io import BytesIO | ||
| from pathlib import Path | ||
| from typing import TYPE_CHECKING, Literal, Optional | ||
|
|
||
| import numpy as np | ||
| from tqdm import tqdm | ||
|
|
||
| if TYPE_CHECKING: | ||
| from .core import AudioModelFamilyV1 | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
|
|
||
| class F5TTSMLXModel: | ||
| def __init__( | ||
| self, | ||
| model_uid: str, | ||
| model_path: str, | ||
| model_spec: "AudioModelFamilyV1", | ||
| device: Optional[str] = None, | ||
| **kwargs, | ||
| ): | ||
| self._model_uid = model_uid | ||
| self._model_path = model_path | ||
| self._model_spec = model_spec | ||
| self._device = device | ||
| self._model = None | ||
| self._kwargs = kwargs | ||
| self._model = None | ||
|
|
||
| @property | ||
| def model_ability(self): | ||
| return self._model_spec.model_ability | ||
|
|
||
| def load(self): | ||
| try: | ||
| import mlx.core as mx | ||
| from f5_tts_mlx.cfm import F5TTS | ||
| from f5_tts_mlx.dit import DiT | ||
| from f5_tts_mlx.duration import DurationPredictor, DurationTransformer | ||
| from vocos_mlx import Vocos | ||
| except ImportError: | ||
| error_message = "Failed to import module 'f5_tts_mlx'" | ||
| installation_guide = [ | ||
| "Please make sure 'f5_tts_mlx' is installed.\n", | ||
| ] | ||
|
|
||
| raise ImportError(f"{error_message}\n\n{''.join(installation_guide)}") | ||
|
|
||
| path = Path(self._model_path) | ||
| # vocab | ||
|
|
||
| vocab_path = path / "vocab.txt" | ||
| vocab = {v: i for i, v in enumerate(Path(vocab_path).read_text().split("\n"))} | ||
| if len(vocab) == 0: | ||
| raise ValueError(f"Could not load vocab from {vocab_path}") | ||
|
|
||
| # duration predictor | ||
|
|
||
| duration_model_path = path / "duration_v2.safetensors" | ||
| duration_predictor = None | ||
|
|
||
| if duration_model_path.exists(): | ||
| duration_predictor = DurationPredictor( | ||
| transformer=DurationTransformer( | ||
| dim=512, | ||
| depth=8, | ||
| heads=8, | ||
| text_dim=512, | ||
| ff_mult=2, | ||
| conv_layers=2, | ||
| text_num_embeds=len(vocab) - 1, | ||
| ), | ||
| vocab_char_map=vocab, | ||
| ) | ||
| weights = mx.load(duration_model_path.as_posix(), format="safetensors") | ||
| duration_predictor.load_weights(list(weights.items())) | ||
|
|
||
| # vocoder | ||
|
|
||
| vocos = Vocos.from_pretrained("lucasnewman/vocos-mel-24khz") | ||
|
|
||
| # model | ||
|
|
||
| model_path = path / "model.safetensors" | ||
|
|
||
| f5tts = F5TTS( | ||
| transformer=DiT( | ||
| dim=1024, | ||
| depth=22, | ||
| heads=16, | ||
| ff_mult=2, | ||
| text_dim=512, | ||
| conv_layers=4, | ||
| text_num_embeds=len(vocab) - 1, | ||
| ), | ||
| vocab_char_map=vocab, | ||
| vocoder=vocos.decode, | ||
| duration_predictor=duration_predictor, | ||
| ) | ||
|
|
||
| weights = mx.load(model_path.as_posix(), format="safetensors") | ||
| f5tts.load_weights(list(weights.items())) | ||
| mx.eval(f5tts.parameters()) | ||
|
|
||
| self._model = f5tts | ||
|
|
||
| def speech( | ||
| self, | ||
| input: str, | ||
| voice: str, | ||
| response_format: str = "mp3", | ||
| speed: float = 1.0, | ||
| stream: bool = False, | ||
| **kwargs, | ||
| ): | ||
| import mlx.core as mx | ||
| import soundfile as sf | ||
| import tomli | ||
| from f5_tts_mlx.generate import ( | ||
| FRAMES_PER_SEC, | ||
| SAMPLE_RATE, | ||
| TARGET_RMS, | ||
| convert_char_to_pinyin, | ||
| split_sentences, | ||
| ) | ||
|
|
||
| if stream: | ||
| raise Exception("F5-TTS does not support stream generation.") | ||
|
|
||
| prompt_speech: Optional[bytes] = kwargs.pop("prompt_speech", None) | ||
| prompt_text: Optional[str] = kwargs.pop("prompt_text", None) | ||
| duration: Optional[float] = kwargs.pop("duration", None) | ||
| steps: Optional[int] = kwargs.pop("steps", 8) | ||
| cfg_strength: Optional[float] = kwargs.pop("cfg_strength", 2.0) | ||
| method: Literal["euler", "midpoint"] = kwargs.pop("method", "rk4") | ||
| sway_sampling_coef: float = kwargs.pop("sway_sampling_coef", -1.0) | ||
| seed: Optional[int] = kwargs.pop("seed", None) | ||
|
|
||
| if prompt_speech is None: | ||
| base = os.path.join(os.path.dirname(__file__), "../../thirdparty/f5_tts") | ||
| config = os.path.join(base, "infer/examples/basic/basic.toml") | ||
| with open(config, "rb") as f: | ||
| config_dict = tomli.load(f) | ||
| prompt_speech_path = os.path.join(base, config_dict["ref_audio"]) | ||
| prompt_text = config_dict["ref_text"] | ||
| else: | ||
| with tempfile.NamedTemporaryFile(delete=False) as f: # type: ignore | ||
| f.write(prompt_speech) | ||
| prompt_speech_path = f.name | ||
|
|
||
| if prompt_text is None: | ||
| raise ValueError("`prompt_text` cannot be empty") | ||
|
|
||
| audio, sr = sf.read(prompt_speech_path) | ||
| audio = mx.array(audio) | ||
| ref_audio_duration = audio.shape[0] / SAMPLE_RATE | ||
| logger.debug( | ||
| f"Got reference audio with duration: {ref_audio_duration:.2f} seconds" | ||
| ) | ||
|
|
||
| rms = mx.sqrt(mx.mean(mx.square(audio))) | ||
| if rms < TARGET_RMS: | ||
| audio = audio * TARGET_RMS / rms | ||
|
|
||
| sentences = split_sentences(input) | ||
| is_single_generation = len(sentences) <= 1 or duration is not None | ||
|
|
||
| if is_single_generation: | ||
| generation_text = convert_char_to_pinyin([prompt_text + " " + input]) # type: ignore | ||
|
|
||
| if duration is not None: | ||
| duration = int(duration * FRAMES_PER_SEC) | ||
|
|
||
| start_date = datetime.datetime.now() | ||
|
|
||
| wave, _ = self._model.sample( # type: ignore | ||
| mx.expand_dims(audio, axis=0), | ||
| text=generation_text, | ||
| duration=duration, | ||
| steps=steps, | ||
| method=method, | ||
| speed=speed, | ||
| cfg_strength=cfg_strength, | ||
| sway_sampling_coef=sway_sampling_coef, | ||
| seed=seed, | ||
| ) | ||
|
|
||
| wave = wave[audio.shape[0] :] | ||
| mx.eval(wave) | ||
|
|
||
| generated_duration = wave.shape[0] / SAMPLE_RATE | ||
| print( | ||
| f"Generated {generated_duration:.2f}s of audio in {datetime.datetime.now() - start_date}." | ||
| ) | ||
|
|
||
| else: | ||
| start_date = datetime.datetime.now() | ||
|
|
||
| output = [] | ||
|
|
||
| for sentence_text in tqdm(split_sentences(input)): | ||
| text = convert_char_to_pinyin([prompt_text + " " + sentence_text]) # type: ignore | ||
|
|
||
| if duration is not None: | ||
| duration = int(duration * FRAMES_PER_SEC) | ||
|
|
||
| wave, _ = self._model.sample( # type: ignore | ||
| mx.expand_dims(audio, axis=0), | ||
| text=text, | ||
| duration=duration, | ||
| steps=steps, | ||
| method=method, | ||
| speed=speed, | ||
| cfg_strength=cfg_strength, | ||
| sway_sampling_coef=sway_sampling_coef, | ||
| seed=seed, | ||
| ) | ||
|
|
||
| # trim the reference audio | ||
| wave = wave[audio.shape[0] :] | ||
| mx.eval(wave) | ||
|
|
||
| output.append(wave) | ||
|
|
||
| wave = mx.concatenate(output, axis=0) | ||
|
|
||
| generated_duration = wave.shape[0] / SAMPLE_RATE | ||
| logger.debug( | ||
| f"Generated {generated_duration:.2f}s of audio in {datetime.datetime.now() - start_date}." | ||
| ) | ||
|
|
||
| # Save the generated audio | ||
| with BytesIO() as out: | ||
| with sf.SoundFile( | ||
| out, "w", SAMPLE_RATE, 1, format=response_format.upper() | ||
| ) as f: | ||
| f.write(np.array(wave)) | ||
| return out.getvalue() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,50 @@ | ||
| # Copyright 2022-2023 XProbe Inc. | ||
| # | ||
| # 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. | ||
|
|
||
| import os | ||
| import tempfile | ||
|
|
||
|
|
||
| def test_f5tts_mlx(setup): | ||
| endpoint, _ = setup | ||
| from ....client import Client | ||
|
|
||
| client = Client(endpoint) | ||
|
|
||
| model_uid = client.launch_model( | ||
| model_name="F5-TTS-MLX", | ||
| model_type="audio", | ||
| download_hub="huggingface", | ||
| ) | ||
| model = client.get_model(model_uid) | ||
| input_string = ( | ||
| "chat T T S is a text to speech model designed for dialogue applications." | ||
| ) | ||
| response = model.speech(input_string) | ||
| assert type(response) is bytes | ||
| assert len(response) > 0 | ||
|
|
||
| with tempfile.NamedTemporaryFile(suffix=".mp3", delete=True) as f: | ||
| f.write(response) | ||
|
|
||
| # Test openai API | ||
| import openai | ||
|
|
||
| client = openai.Client(api_key="not empty", base_url=f"{endpoint}/v1") | ||
| with client.audio.speech.with_streaming_response.create( | ||
| model=model_uid, input=input_string, voice="echo" | ||
| ) as response: | ||
| with tempfile.NamedTemporaryFile(suffix=".mp3", delete=True) as f: | ||
| response.stream_to_file(f.name) | ||
| assert os.stat(f.name).st_size > 0 |