mirror of https://github.com/coqui-ai/TTS.git
add ability to pass tortoise presets through coqui api
This commit is contained in:
manmay-nakhashi
parent
8c739fd5f2
commit
fede89ac0d
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@ -504,6 +504,7 @@ class TTS:
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speaker_wav: str = None,
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emotion: str = None,
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speed: float = None,
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**kwargs,
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):
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"""Convert text to speech.
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@ -540,6 +541,7 @@ class TTS:
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style_wav=None,
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style_text=None,
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reference_speaker_name=None,
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**kwargs,
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)
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return wav
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@ -552,6 +554,7 @@ class TTS:
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emotion: str = "Neutral",
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speed: float = 1.0,
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file_path: str = "output.wav",
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**kwargs,
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):
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"""Convert text to speech.
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@ -580,7 +583,7 @@ class TTS:
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return self.tts_coqui_studio(
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text=text, speaker_name=speaker, language=language, emotion=emotion, speed=speed, file_path=file_path
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)
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wav = self.tts(text=text, speaker=speaker, language=language, speaker_wav=speaker_wav)
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wav = self.tts(text=text, speaker=speaker, language=language, speaker_wav=speaker_wav, **kwargs)
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self.synthesizer.save_wav(wav=wav, path=file_path)
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return file_path
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@ -1,8 +1,8 @@
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import functools
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import math
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import os
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import fsspec
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import fsspec
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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@ -2,8 +2,8 @@ import os
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import torch
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from tokenizers import Tokenizer
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from TTS.tts.utils.text.cleaners import english_cleaners
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from TTS.tts.utils.text.cleaners import english_cleaners
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DEFAULT_VOCAB_FILE = os.path.join(
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os.path.dirname(os.path.realpath(__file__)), "../../utils/assets/tortoise/tokenizer.json"
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@ -5,20 +5,17 @@ from tqdm import tqdm
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DEFAULT_MODELS_DIR = os.path.join(os.path.expanduser("~"), ".cache", "tortoise", "models")
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MODELS_DIR = os.environ.get("TORTOISE_MODELS_DIR", DEFAULT_MODELS_DIR)
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MODELS_DIR = "/data/speech_synth/models/"
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MODELS = {
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"autoregressive.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth",
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"classifier.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/classifier.pth",
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"clvp2.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/clvp2.pth",
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"cvvp.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/cvvp.pth",
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"diffusion_decoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/diffusion_decoder.pth",
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"vocoder.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/vocoder.pth",
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"rlg_auto.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth",
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"rlg_diffuser.pth": "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth",
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}
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pbar = None
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def download_models(specific_models=None):
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"""
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@ -11,13 +11,7 @@ from coqpit import Coqpit
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from tqdm import tqdm
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from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram
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from TTS.tts.layers.tortoise.audio_utils import (
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denormalize_tacotron_mel,
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load_audio,
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load_voice,
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load_voices,
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wav_to_univnet_mel,
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)
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from TTS.tts.layers.tortoise.audio_utils import denormalize_tacotron_mel, load_voice, wav_to_univnet_mel
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from TTS.tts.layers.tortoise.autoregressive import UnifiedVoice
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from TTS.tts.layers.tortoise.classifier import AudioMiniEncoderWithClassifierHead
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from TTS.tts.layers.tortoise.clvp import CLVP
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@ -25,7 +19,7 @@ from TTS.tts.layers.tortoise.diffusion import SpacedDiffusion, get_named_beta_sc
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from TTS.tts.layers.tortoise.diffusion_decoder import DiffusionTts
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from TTS.tts.layers.tortoise.random_latent_generator import RandomLatentConverter
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from TTS.tts.layers.tortoise.tokenizer import VoiceBpeTokenizer
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from TTS.tts.layers.tortoise.utils import MODELS_DIR, get_model_path
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from TTS.tts.layers.tortoise.utils import get_model_path
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from TTS.tts.layers.tortoise.vocoder import VocConf
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from TTS.tts.layers.tortoise.wav2vec_alignment import Wav2VecAlignment
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from TTS.tts.models.base_tts import BaseTTS
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@ -498,16 +492,11 @@ class Tortoise(BaseTTS):
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with torch.no_grad():
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return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
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def synthesis(
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self,
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text,
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config,
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speaker_id="lj",
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):
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def synthesis(self, text, config, speaker_id="lj", **kwargs):
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voice_samples, conditioning_latents = load_voice(speaker_id)
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outputs = self.inference_with_config(
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text, config, voice_samples=voice_samples, conditioning_latents=conditioning_latents
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text, config, voice_samples=voice_samples, conditioning_latents=conditioning_latents, **kwargs
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)
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return_dict = {
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@ -533,10 +522,47 @@ class Tortoise(BaseTTS):
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"top_p": config.top_p,
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"cond_free_k": config.cond_free_k,
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"diffusion_temperature": config.diffusion_temperature,
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"num_autoregressive_samples": config.num_autoregressive_samples,
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"diffusion_iterations": config.diffusion_iterations,
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"sampler": config.sampler,
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}
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# Presets are defined here.
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presets = {
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"single_sample": {
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"num_autoregressive_samples": 8,
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"diffusion_iterations": 10,
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"sampler": "ddim",
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},
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"ultra_fast": {
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"num_autoregressive_samples": 16,
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"diffusion_iterations": 10,
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"sampler": "ddim",
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},
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"ultra_fast_old": {
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"num_autoregressive_samples": 16,
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"diffusion_iterations": 30,
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"cond_free": False,
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},
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"very_fast": {
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"num_autoregressive_samples": 32,
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"diffusion_iterations": 30,
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"sampler": "dpm++2m",
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},
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"fast": {
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"num_autoregressive_samples": 5,
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"diffusion_iterations": 50,
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"sampler": "ddim",
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},
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"fast_old": {"num_autoregressive_samples": 96, "diffusion_iterations": 80},
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"standard": {
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"num_autoregressive_samples": 5,
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"diffusion_iterations": 200,
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},
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"high_quality": {
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"num_autoregressive_samples": 256,
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"diffusion_iterations": 400,
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},
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}
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settings.update(presets[kwargs["preset"]])
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kwargs.pop("preset")
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settings.update(kwargs) # allow overriding of preset settings with kwargs
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return self.inference(text, **settings)
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@ -551,7 +577,7 @@ class Tortoise(BaseTTS):
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return_deterministic_state=False,
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latent_averaging_mode=0,
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# autoregressive generation parameters follow
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num_autoregressive_samples=512,
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num_autoregressive_samples=16,
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temperature=0.8,
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length_penalty=1,
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repetition_penalty=2.0,
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@ -228,6 +228,7 @@ class Synthesizer(object):
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style_text=None,
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reference_wav=None,
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reference_speaker_name=None,
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**kwargs,
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) -> List[int]:
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"""🐸 TTS magic. Run all the models and generate speech.
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@ -328,7 +329,7 @@ class Synthesizer(object):
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if not reference_wav:
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for sen in sens:
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if self.tts_config.model == "tortoise":
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outputs = self.tts_model.synthesis(text=sen, config=self.tts_config)
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outputs = self.tts_model.synthesis(text=sen, config=self.tts_config, **kwargs)
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else:
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# synthesize voice
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outputs = synthesis(
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