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FEAT: Support F5 TTS (xorbitsai#2626)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,195 @@ | ||
| # 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 logging | ||
| import os | ||
| import re | ||
| from io import BytesIO | ||
| from typing import TYPE_CHECKING, Optional | ||
|
|
||
| if TYPE_CHECKING: | ||
| from .core import AudioModelFamilyV1 | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
|
|
||
| class F5TTSModel: | ||
| 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._vocoder = None | ||
| self._kwargs = kwargs | ||
|
|
||
| @property | ||
| def model_ability(self): | ||
| return self._model_spec.model_ability | ||
|
|
||
| def load(self): | ||
| import os | ||
| import sys | ||
|
|
||
| # The yaml config loaded from model has hard-coded the import paths. please refer to: load_hyperpyyaml | ||
| sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../thirdparty")) | ||
|
|
||
| from f5_tts.infer.utils_infer import load_model, load_vocoder | ||
| from f5_tts.model import DiT | ||
|
|
||
| vocoder_name = self._kwargs.get("vocoder_name", "vocos") | ||
| vocoder_path = self._kwargs.get("vocoder_path") | ||
|
|
||
| if vocoder_name not in ["vocos", "bigvgan"]: | ||
| raise Exception(f"Unsupported vocoder name: {vocoder_name}") | ||
|
|
||
| if vocoder_path is not None: | ||
| self._vocoder = load_vocoder( | ||
| vocoder_name=vocoder_name, is_local=True, local_path=vocoder_path | ||
| ) | ||
| else: | ||
| self._vocoder = load_vocoder(vocoder_name=vocoder_name, is_local=False) | ||
|
|
||
| model_cls = DiT | ||
| model_cfg = dict( | ||
| dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4 | ||
| ) | ||
| if vocoder_name == "vocos": | ||
| exp_name = "F5TTS_Base" | ||
| ckpt_step = 1200000 | ||
| elif vocoder_name == "bigvgan": | ||
| exp_name = "F5TTS_Base_bigvgan" | ||
| ckpt_step = 1250000 | ||
| else: | ||
| assert False | ||
| ckpt_file = os.path.join( | ||
| self._model_path, exp_name, f"model_{ckpt_step}.safetensors" | ||
| ) | ||
| logger.info(f"Loading %s...", ckpt_file) | ||
| self._model = load_model( | ||
| model_cls, model_cfg, ckpt_file, mel_spec_type=vocoder_name | ||
| ) | ||
|
|
||
| def _infer(self, ref_audio, ref_text, text_gen, model_obj, mel_spec_type, speed): | ||
| import numpy as np | ||
| from f5_tts.infer.utils_infer import infer_process, preprocess_ref_audio_text | ||
|
|
||
| config = {} | ||
| main_voice = {"ref_audio": ref_audio, "ref_text": ref_text} | ||
| if "voices" not in config: | ||
| voices = {"main": main_voice} | ||
| else: | ||
| voices = config["voices"] | ||
| voices["main"] = main_voice | ||
| for voice in voices: | ||
| ( | ||
| voices[voice]["ref_audio"], | ||
| voices[voice]["ref_text"], | ||
| ) = preprocess_ref_audio_text( | ||
| voices[voice]["ref_audio"], voices[voice]["ref_text"] | ||
| ) | ||
| print("Voice:", voice) | ||
| print("Ref_audio:", voices[voice]["ref_audio"]) | ||
| print("Ref_text:", voices[voice]["ref_text"]) | ||
|
|
||
| final_sample_rate = None | ||
| generated_audio_segments = [] | ||
| reg1 = r"(?=\[\w+\])" | ||
| chunks = re.split(reg1, text_gen) | ||
| reg2 = r"\[(\w+)\]" | ||
| for text in chunks: | ||
| if not text.strip(): | ||
| continue | ||
| match = re.match(reg2, text) | ||
| if match: | ||
| voice = match[1] | ||
| else: | ||
| print("No voice tag found, using main.") | ||
| voice = "main" | ||
| if voice not in voices: | ||
| print(f"Voice {voice} not found, using main.") | ||
| voice = "main" | ||
| text = re.sub(reg2, "", text) | ||
| gen_text = text.strip() | ||
| ref_audio = voices[voice]["ref_audio"] | ||
| ref_text = voices[voice]["ref_text"] | ||
| print(f"Voice: {voice}") | ||
| audio, final_sample_rate, spectragram = infer_process( | ||
| ref_audio, | ||
| ref_text, | ||
| gen_text, | ||
| model_obj, | ||
| self._vocoder, | ||
| mel_spec_type=mel_spec_type, | ||
| speed=speed, | ||
| ) | ||
| generated_audio_segments.append(audio) | ||
|
|
||
| if generated_audio_segments: | ||
| final_wave = np.concatenate(generated_audio_segments) | ||
| return final_sample_rate, final_wave | ||
| return None, None | ||
|
|
||
| def speech( | ||
| self, | ||
| input: str, | ||
| voice: str, | ||
| response_format: str = "mp3", | ||
| speed: float = 1.0, | ||
| stream: bool = False, | ||
| **kwargs, | ||
| ): | ||
| import f5_tts | ||
| import soundfile | ||
| import tomli | ||
|
|
||
| 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) | ||
|
|
||
| if prompt_speech is None: | ||
| base = os.path.dirname(f5_tts.__file__) | ||
| config = os.path.join(base, "infer/examples/basic/basic.toml") | ||
| with open(config, "rb") as f: | ||
| config_dict = tomli.load(f) | ||
| prompt_speech = os.path.join(base, config_dict["ref_audio"]) | ||
| prompt_text = config_dict["ref_text"] | ||
|
|
||
| assert self._model is not None | ||
| vocoder_name = self._kwargs.get("vocoder_name", "vocos") | ||
| sample_rate, wav = self._infer( | ||
| ref_audio=prompt_speech, | ||
| ref_text=prompt_text, | ||
| text_gen=input, | ||
| model_obj=self._model, | ||
| mel_spec_type=vocoder_name, | ||
| speed=speed, | ||
| ) | ||
|
|
||
| # Save the generated audio | ||
| with BytesIO() as out: | ||
| with soundfile.SoundFile( | ||
| out, "w", sample_rate, 1, format=response_format.upper() | ||
| ) as f: | ||
| f.write(wav) | ||
| return out.getvalue() |
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