mirror of https://github.com/coqui-ai/TTS.git
816 lines
35 KiB
Python
816 lines
35 KiB
Python
import os
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import random
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from contextlib import contextmanager
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from dataclasses import dataclass
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from time import time
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import torch
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import torch.nn.functional as F
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import torchaudio
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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 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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from TTS.tts.layers.tortoise.diffusion import SpacedDiffusion, get_named_beta_schedule, space_timesteps
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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 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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def pad_or_truncate(t, length):
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"""
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Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s.
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"""
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tp = t[..., :length]
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if t.shape[-1] == length:
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tp = t
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elif t.shape[-1] < length:
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tp = F.pad(t, (0, length - t.shape[-1]))
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return tp
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def deterministic_state(seed=None):
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"""
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Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
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reproduced.
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"""
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seed = int(time()) if seed is None else seed
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torch.manual_seed(seed)
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random.seed(seed)
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# Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
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# torch.use_deterministic_algorithms(True)
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return seed
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def load_discrete_vocoder_diffuser(
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trained_diffusion_steps=4000,
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desired_diffusion_steps=200,
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cond_free=True,
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cond_free_k=1,
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sampler="ddim",
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):
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"""
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Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
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"""
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return SpacedDiffusion(
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use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]),
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model_mean_type="epsilon",
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model_var_type="learned_range",
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loss_type="mse",
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betas=get_named_beta_schedule("linear", trained_diffusion_steps),
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conditioning_free=cond_free,
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conditioning_free_k=cond_free_k,
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sampler=sampler,
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)
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def format_conditioning(clip, cond_length=132300, device="cuda"):
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"""
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Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models.
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"""
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gap = clip.shape[-1] - cond_length
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if gap < 0:
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clip = F.pad(clip, pad=(0, abs(gap)))
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elif gap > 0:
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rand_start = random.randint(0, gap)
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clip = clip[:, rand_start : rand_start + cond_length]
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mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0)
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return mel_clip.unsqueeze(0).to(device)
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def fix_autoregressive_output(codes, stop_token, complain=True):
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"""
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This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
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trained on and what the autoregressive code generator creates (which has no padding or end).
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This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
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a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
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and copying out the last few codes.
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Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
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"""
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# Strip off the autoregressive stop token and add padding.
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stop_token_indices = (codes == stop_token).nonzero()
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if len(stop_token_indices) == 0:
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if complain:
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print(
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"No stop tokens found in one of the generated voice clips. This typically means the spoken audio is "
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"too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, "
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"try breaking up your input text."
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)
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return codes
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codes[stop_token_indices] = 83
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stm = stop_token_indices.min().item()
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codes[stm:] = 83
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if stm - 3 < codes.shape[0]:
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codes[-3] = 45
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codes[-2] = 45
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codes[-1] = 248
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return codes
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def do_spectrogram_diffusion(
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diffusion_model,
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diffuser,
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latents,
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conditioning_latents,
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temperature=1,
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verbose=True,
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):
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"""
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Uses the specified diffusion model to convert discrete codes into a spectrogram.
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"""
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with torch.no_grad():
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output_seq_len = (
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latents.shape[1] * 4 * 24000 // 22050
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) # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
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output_shape = (latents.shape[0], 100, output_seq_len)
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precomputed_embeddings = diffusion_model.timestep_independent(
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latents, conditioning_latents, output_seq_len, False
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)
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noise = torch.randn(output_shape, device=latents.device) * temperature
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mel = diffuser.sample_loop(
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diffusion_model,
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output_shape,
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noise=noise,
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model_kwargs={"precomputed_aligned_embeddings": precomputed_embeddings},
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progress=verbose,
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)
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return denormalize_tacotron_mel(mel)[:, :, :output_seq_len]
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def classify_audio_clip(clip):
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"""
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Returns whether or not Tortoises' classifier thinks the given clip came from Tortoise.
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:param clip: torch tensor containing audio waveform data (get it from load_audio)
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:return: True if the clip was classified as coming from Tortoise and false if it was classified as real.
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"""
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classifier = AudioMiniEncoderWithClassifierHead(
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2,
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spec_dim=1,
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embedding_dim=512,
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depth=5,
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downsample_factor=4,
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resnet_blocks=2,
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attn_blocks=4,
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num_attn_heads=4,
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base_channels=32,
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dropout=0,
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kernel_size=5,
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distribute_zero_label=False,
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)
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classifier.load_state_dict(torch.load(get_model_path("classifier.pth"), map_location=torch.device("cpu")))
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clip = clip.cpu().unsqueeze(0)
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results = F.softmax(classifier(clip), dim=-1)
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return results[0][0]
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def pick_best_batch_size_for_gpu():
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"""
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Tries to pick a batch size that will fit in your GPU. These sizes aren't guaranteed to work, but they should give
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you a good shot.
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"""
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if torch.cuda.is_available():
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_, available = torch.cuda.mem_get_info()
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availableGb = available / (1024**3)
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batch_size = 1
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if availableGb > 14:
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batch_size = 16
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elif availableGb > 10:
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batch_size = 8
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elif availableGb > 7:
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batch_size = 4
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return batch_size
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def init_from_config(config: "TortoiseConfig"):
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tts = Tortoise(config)
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return tts
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@dataclass
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class TortoiseAudioConfig(Coqpit):
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sample_rate: int = 22050
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diffusion_sample_rate: int = 24000
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output_sample_rate: int = 24000
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@dataclass
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class TortoiseArgs(Coqpit):
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autoregressive_batch_size: int = 1
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enable_redaction: bool = True
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high_vram: bool = False
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kv_cache: bool = True
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ar_checkpoint: str = None
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clvp_checkpoint: str = None
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diff_checkpoint: str = None
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num_chars: int = 255
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# UnifiedVoice params
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ar_max_mel_tokens: int = 604
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ar_max_text_tokens: int = 402
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ar_max_conditioning_inputs: int = 2
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ar_layers: int = 30
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ar_model_dim: int = 1024
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ar_heads: int = 16
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ar_number_text_tokens: int = 255
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ar_start_text_token: int = 255
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ar_checkpointing: bool = False
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ar_train_solo_embeddings: bool = False
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# DiffTTS params
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diff_model_channels: int = 1024
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diff_num_layers: int = 10
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diff_in_channels: int = 100
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diff_out_channels: int = 200
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diff_in_latent_channels: int = 1024
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diff_in_tokens: int = 8193
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diff_dropout: int = 0
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diff_use_fp16: bool = False
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diff_num_heads: int = 16
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diff_layer_drop: int = 0
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diff_unconditioned_percentage: int = 0
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# clvp params
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clvp_dim_text: int = 768
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clvp_dim_speech: int = 768
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clvp_dim_latent: int = 768
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clvp_num_text_tokens: int = 256
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clvp_text_enc_depth: int = 20
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clvp_text_seq_len: int = 350
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clvp_text_heads: int = 12
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clvp_num_speech_tokens: int = 8192
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clvp_speech_enc_depth: int = 20
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clvp_speech_heads: int = 12
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clvp_speech_seq_len: int = 430
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clvp_use_xformers: bool = True
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# constants
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duration_const: int = 102400
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class Tortoise(BaseTTS):
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"""
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Main entry point into Tortoise.
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"""
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def __init__(
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self,
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config: Coqpit,
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vocoder=VocConf.Univnet,
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):
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"""
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Constructor
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:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
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GPU OOM errors. Larger numbers generates slightly faster.
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:param models_dir: Where model weights are stored. This should only be specified if you are providing your own
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models, otherwise use the defaults.
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:param enable_redaction: When true, text enclosed in brackets are automatically redacted from the spoken output
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(but are still rendered by the model). This can be used for prompt engineering.
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Default is true.
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:param device: Device to use when running the model. If omitted, the device will be automatically chosen.
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:param high_vram: If true, the model will use more VRAM but will run faster.
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:param kv_cache: If true, the autoregressive model will cache key value attention pairs to speed up generation.
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:param ar_checkpoint: Path to a checkpoint file for the autoregressive model. If omitted, uses default
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:param clvp_checkpoint: Path to a checkpoint file for the CLVP model. If omitted, uses default
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:param diff_checkpoint: Path to a checkpoint file for the diffusion model. If omitted, uses default
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"""
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super().__init__(config, ap=None, tokenizer=None)
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self.config = config
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self.ar_checkpoint = self.args.ar_checkpoint
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self.diff_checkpoint = self.args.diff_checkpoint # TODO: check if this is even needed
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self.models_dir = config.model_dir
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self.autoregressive_batch_size = (
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pick_best_batch_size_for_gpu()
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if self.args.autoregressive_batch_size is None
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else self.args.autoregressive_batch_size
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)
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self.enable_redaction = self.args.enable_redaction
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if self.enable_redaction:
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self.aligner = Wav2VecAlignment()
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self.tokenizer = VoiceBpeTokenizer()
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self.autoregressive = (
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UnifiedVoice(
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max_mel_tokens=self.args.ar_max_mel_tokens,
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max_text_tokens=self.args.ar_max_text_tokens,
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max_conditioning_inputs=self.args.ar_max_conditioning_inputs,
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layers=self.args.ar_layers,
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model_dim=self.args.ar_model_dim,
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heads=self.args.ar_heads,
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number_text_tokens=self.args.ar_number_text_tokens,
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start_text_token=self.args.ar_start_text_token,
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checkpointing=self.args.ar_checkpointing,
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train_solo_embeddings=self.args.ar_train_solo_embeddings,
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)
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.cpu()
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.eval()
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)
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ar_path = self.args.ar_checkpoint or get_model_path("autoregressive.pth", self.models_dir)
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self.autoregressive.load_state_dict(torch.load(ar_path))
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self.autoregressive.post_init_gpt2_config(self.args.kv_cache)
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diff_path = self.args.diff_checkpoint or get_model_path("diffusion_decoder.pth", self.models_dir)
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self.diffusion = (
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DiffusionTts(
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model_channels=self.args.diff_model_channels,
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num_layers=self.args.diff_num_layers,
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in_channels=self.args.diff_in_channels,
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out_channels=self.args.diff_out_channels,
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in_latent_channels=self.args.diff_in_latent_channels,
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in_tokens=self.args.diff_in_tokens,
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dropout=self.args.diff_dropout,
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use_fp16=self.args.diff_use_fp16,
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num_heads=self.args.diff_num_heads,
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layer_drop=self.args.diff_layer_drop,
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unconditioned_percentage=self.args.diff_unconditioned_percentage,
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)
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.cpu()
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.eval()
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)
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self.diffusion.load_state_dict(torch.load(diff_path))
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self.clvp = (
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CLVP(
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dim_text=self.args.clvp_dim_text,
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dim_speech=self.args.clvp_dim_speech,
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dim_latent=self.args.clvp_dim_latent,
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num_text_tokens=self.args.clvp_num_text_tokens,
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text_enc_depth=self.args.clvp_text_enc_depth,
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text_seq_len=self.args.clvp_text_seq_len,
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text_heads=self.args.clvp_text_heads,
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num_speech_tokens=self.args.clvp_num_speech_tokens,
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speech_enc_depth=self.args.clvp_speech_enc_depth,
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speech_heads=self.args.clvp_speech_heads,
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speech_seq_len=self.args.clvp_speech_seq_len,
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use_xformers=self.args.clvp_use_xformers,
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)
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.cpu()
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.eval()
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)
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clvp_path = self.args.clvp_checkpoint or get_model_path("clvp2.pth", self.models_dir)
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self.clvp.load_state_dict(torch.load(clvp_path))
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self.vocoder = vocoder.value.constructor().cpu()
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self.vocoder.load_state_dict(
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vocoder.value.optionally_index(
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torch.load(
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get_model_path(vocoder.value.model_path, self.models_dir),
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map_location=torch.device("cpu"),
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)
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)
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)
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self.vocoder.eval(inference=True)
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# Random latent generators (RLGs) are loaded lazily.
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self.rlg_auto = None
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self.rlg_diffusion = None
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if self.args.high_vram:
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self.autoregressive = self.autoregressive.to(self.device)
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self.diffusion = self.diffusion.to(self.device)
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self.clvp = self.clvp.to(self.device)
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self.vocoder = self.vocoder.to(self.device)
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self.high_vram = self.args.high_vram
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@contextmanager
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def temporary_cuda(self, model):
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if self.high_vram:
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yield model
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else:
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m = model.to(self.device)
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yield m
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m = model.cpu()
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def get_conditioning_latents(
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self,
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voice_samples,
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return_mels=False,
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latent_averaging_mode=0,
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original_tortoise=False,
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):
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"""
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Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
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These are expressive learned latents that encode aspects of the provided clips like voice, intonation, and acoustic
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properties.
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:param voice_samples: List of arbitrary reference clips, which should be *pairs* of torch tensors containing arbitrary kHz waveform data.
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:param latent_averaging_mode: 0/1/2 for following modes:
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0 - latents will be generated as in original tortoise, using ~4.27s from each voice sample, averaging latent across all samples
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1 - latents will be generated using (almost) entire voice samples, averaged across all the ~4.27s chunks
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2 - latents will be generated using (almost) entire voice samples, averaged per voice sample
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"""
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assert latent_averaging_mode in [
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0,
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1,
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2,
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], "latent_averaging mode has to be one of (0, 1, 2)"
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with torch.no_grad():
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voice_samples = [[v.to(self.device) for v in ls] for ls in voice_samples]
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auto_conds = []
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for ls in voice_samples:
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auto_conds.append(format_conditioning(ls[0], device=self.device))
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auto_conds = torch.stack(auto_conds, dim=1)
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with self.temporary_cuda(self.autoregressive) as ar:
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auto_latent = ar.get_conditioning(auto_conds)
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diffusion_conds = []
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DURS_CONST = self.args.duration_const
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for ls in voice_samples:
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# The diffuser operates at a sample rate of 24000 (except for the latent inputs)
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sample = torchaudio.functional.resample(ls[0], 22050, 24000) if original_tortoise else ls[1]
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if latent_averaging_mode == 0:
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sample = pad_or_truncate(sample, DURS_CONST)
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cond_mel = wav_to_univnet_mel(
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sample.to(self.device),
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do_normalization=False,
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device=self.device,
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)
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diffusion_conds.append(cond_mel)
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else:
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from math import ceil
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if latent_averaging_mode == 2:
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temp_diffusion_conds = []
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for chunk in range(ceil(sample.shape[1] / DURS_CONST)):
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current_sample = sample[:, chunk * DURS_CONST : (chunk + 1) * DURS_CONST]
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current_sample = pad_or_truncate(current_sample, DURS_CONST)
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cond_mel = wav_to_univnet_mel(
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current_sample.to(self.device),
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do_normalization=False,
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device=self.device,
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)
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if latent_averaging_mode == 1:
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diffusion_conds.append(cond_mel)
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elif latent_averaging_mode == 2:
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temp_diffusion_conds.append(cond_mel)
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if latent_averaging_mode == 2:
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diffusion_conds.append(torch.stack(temp_diffusion_conds).mean(0))
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|
diffusion_conds = torch.stack(diffusion_conds, dim=1)
|
|
|
|
with self.temporary_cuda(self.diffusion) as diffusion:
|
|
diffusion_latent = diffusion.get_conditioning(diffusion_conds)
|
|
|
|
if return_mels:
|
|
return auto_latent, diffusion_latent, auto_conds, diffusion_conds
|
|
return auto_latent, diffusion_latent
|
|
|
|
def get_random_conditioning_latents(self):
|
|
# Lazy-load the RLG models.
|
|
if self.rlg_auto is None:
|
|
self.rlg_auto = RandomLatentConverter(1024).eval()
|
|
self.rlg_auto.load_state_dict(
|
|
torch.load(
|
|
get_model_path("rlg_auto.pth", self.models_dir),
|
|
map_location=torch.device("cpu"),
|
|
)
|
|
)
|
|
self.rlg_diffusion = RandomLatentConverter(2048).eval()
|
|
self.rlg_diffusion.load_state_dict(
|
|
torch.load(
|
|
get_model_path("rlg_diffuser.pth", self.models_dir),
|
|
map_location=torch.device("cpu"),
|
|
)
|
|
)
|
|
with torch.no_grad():
|
|
return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
|
|
|
|
def synthesis(self, text, config, speaker_id="lj", **kwargs):
|
|
voice_samples, conditioning_latents = load_voice(speaker_id)
|
|
|
|
outputs = self.inference_with_config(
|
|
text, config, voice_samples=voice_samples, conditioning_latents=conditioning_latents, **kwargs
|
|
)
|
|
|
|
return_dict = {
|
|
"wav": outputs["wav"],
|
|
"deterministic_seed": outputs["deterministic_seed"],
|
|
"text_inputs": outputs["text"],
|
|
"voice_samples": outputs["voice_samples"],
|
|
"conditioning_latents": outputs["conditioning_latents"],
|
|
}
|
|
|
|
return return_dict
|
|
|
|
def inference_with_config(self, text, config, **kwargs):
|
|
"""
|
|
inference with config
|
|
#TODO describe in detail
|
|
"""
|
|
# Use generally found best tuning knobs for generation.
|
|
settings = {
|
|
"temperature": config.temperature,
|
|
"length_penalty": config.length_penalty,
|
|
"repetition_penalty": config.repetition_penalty,
|
|
"top_p": config.top_p,
|
|
"cond_free_k": config.cond_free_k,
|
|
"diffusion_temperature": config.diffusion_temperature,
|
|
"sampler": config.sampler,
|
|
}
|
|
# Presets are defined here.
|
|
presets = {
|
|
"single_sample": {
|
|
"num_autoregressive_samples": 8,
|
|
"diffusion_iterations": 10,
|
|
"sampler": "ddim",
|
|
},
|
|
"ultra_fast": {
|
|
"num_autoregressive_samples": 16,
|
|
"diffusion_iterations": 10,
|
|
"sampler": "ddim",
|
|
},
|
|
"ultra_fast_old": {
|
|
"num_autoregressive_samples": 16,
|
|
"diffusion_iterations": 30,
|
|
"cond_free": False,
|
|
},
|
|
"very_fast": {
|
|
"num_autoregressive_samples": 32,
|
|
"diffusion_iterations": 30,
|
|
"sampler": "dpm++2m",
|
|
},
|
|
"fast": {
|
|
"num_autoregressive_samples": 5,
|
|
"diffusion_iterations": 50,
|
|
"sampler": "ddim",
|
|
},
|
|
"fast_old": {"num_autoregressive_samples": 96, "diffusion_iterations": 80},
|
|
"standard": {
|
|
"num_autoregressive_samples": 5,
|
|
"diffusion_iterations": 200,
|
|
},
|
|
"high_quality": {
|
|
"num_autoregressive_samples": 256,
|
|
"diffusion_iterations": 400,
|
|
},
|
|
}
|
|
settings.update(presets[kwargs["preset"]])
|
|
kwargs.pop("preset")
|
|
settings.update(kwargs) # allow overriding of preset settings with kwargs
|
|
return self.inference(text, **settings)
|
|
|
|
def inference(
|
|
self,
|
|
text,
|
|
voice_samples=None,
|
|
conditioning_latents=None,
|
|
k=1,
|
|
verbose=True,
|
|
use_deterministic_seed=None,
|
|
return_deterministic_state=False,
|
|
latent_averaging_mode=0,
|
|
# autoregressive generation parameters follow
|
|
num_autoregressive_samples=16,
|
|
temperature=0.8,
|
|
length_penalty=1,
|
|
repetition_penalty=2.0,
|
|
top_p=0.8,
|
|
max_mel_tokens=500,
|
|
# diffusion generation parameters follow
|
|
diffusion_iterations=100,
|
|
cond_free=True,
|
|
cond_free_k=2,
|
|
diffusion_temperature=1.0,
|
|
sampler="ddim",
|
|
half=True,
|
|
original_tortoise=False,
|
|
**hf_generate_kwargs,
|
|
):
|
|
"""
|
|
Produces an audio clip of the given text being spoken with the given reference voice.
|
|
:param text: Text to be spoken.
|
|
:param voice_samples: List of an arbitrary number of reference clips, which should be *tuple-pairs* of torch tensors containing arbitrary kHz waveform data.
|
|
:param conditioning_latents: A tuple of (autoregressive_conditioning_latent, diffusion_conditioning_latent), which
|
|
can be provided in lieu of voice_samples. This is ignored unless voice_samples=None.
|
|
Conditioning latents can be retrieved via get_conditioning_latents().
|
|
:param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP model) clips are returned.
|
|
:param latent_averaging_mode: 0/1/2 for following modes:
|
|
0 - latents will be generated as in original tortoise, using ~4.27s from each voice sample, averaging latent across all samples
|
|
1 - latents will be generated using (almost) entire voice samples, averaged across all the ~4.27s chunks
|
|
2 - latents will be generated using (almost) entire voice samples, averaged per voice sample
|
|
:param verbose: Whether or not to print log messages indicating the progress of creating a clip. Default=true.
|
|
~~AUTOREGRESSIVE KNOBS~~
|
|
:param num_autoregressive_samples: Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
|
|
As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
|
|
:param temperature: The softmax temperature of the autoregressive model.
|
|
:param length_penalty: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.
|
|
:param repetition_penalty: A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence
|
|
of long silences or "uhhhhhhs", etc.
|
|
:param top_p: P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely" (aka boring) outputs.
|
|
:param max_mel_tokens: Restricts the output length. (0,600] integer. Each unit is 1/20 of a second.
|
|
:param typical_sampling: Turns typical sampling on or off. This sampling mode is discussed in this paper: https://arxiv.org/abs/2202.00666
|
|
I was interested in the premise, but the results were not as good as I was hoping. This is off by default, but
|
|
could use some tuning.
|
|
:param typical_mass: The typical_mass parameter from the typical_sampling algorithm.
|
|
~~DIFFUSION KNOBS~~
|
|
:param diffusion_iterations: Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
|
|
the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
|
|
however.
|
|
:param cond_free: Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for
|
|
each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output
|
|
of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and
|
|
dramatically improves realism.
|
|
:param cond_free_k: Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
|
|
As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
|
|
Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k
|
|
:param diffusion_temperature: Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
|
|
are the "mean" prediction of the diffusion network and will sound bland and smeared.
|
|
~~OTHER STUFF~~
|
|
:param hf_generate_kwargs: The huggingface Transformers generate API is used for the autoregressive transformer.
|
|
Extra keyword args fed to this function get forwarded directly to that API. Documentation
|
|
here: https://huggingface.co/docs/transformers/internal/generation_utils
|
|
:return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
|
|
Sample rate is 24kHz.
|
|
"""
|
|
deterministic_seed = deterministic_state(seed=use_deterministic_seed)
|
|
|
|
text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device)
|
|
text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary.
|
|
assert (
|
|
text_tokens.shape[-1] < 400
|
|
), "Too much text provided. Break the text up into separate segments and re-try inference."
|
|
|
|
if voice_samples is not None:
|
|
(
|
|
auto_conditioning,
|
|
diffusion_conditioning,
|
|
_,
|
|
_,
|
|
) = self.get_conditioning_latents(
|
|
voice_samples,
|
|
return_mels=True,
|
|
latent_averaging_mode=latent_averaging_mode,
|
|
original_tortoise=original_tortoise,
|
|
)
|
|
elif conditioning_latents is not None:
|
|
auto_conditioning, diffusion_conditioning = conditioning_latents
|
|
else:
|
|
(
|
|
auto_conditioning,
|
|
diffusion_conditioning,
|
|
) = self.get_random_conditioning_latents()
|
|
auto_conditioning = auto_conditioning.to(self.device)
|
|
diffusion_conditioning = diffusion_conditioning.to(self.device)
|
|
|
|
diffuser = load_discrete_vocoder_diffuser(
|
|
desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k, sampler=sampler
|
|
)
|
|
|
|
# in the case of single_sample,
|
|
orig_batch_size = self.autoregressive_batch_size
|
|
while num_autoregressive_samples % self.autoregressive_batch_size:
|
|
self.autoregressive_batch_size //= 2
|
|
with torch.no_grad():
|
|
samples = []
|
|
num_batches = num_autoregressive_samples // self.autoregressive_batch_size
|
|
stop_mel_token = self.autoregressive.stop_mel_token
|
|
calm_token = (
|
|
83 # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
|
|
)
|
|
self.autoregressive = self.autoregressive.to(self.device)
|
|
if verbose:
|
|
print("Generating autoregressive samples..")
|
|
with self.temporary_cuda(self.autoregressive) as autoregressive, torch.autocast(
|
|
device_type="cuda", dtype=torch.float16, enabled=half
|
|
):
|
|
for b in tqdm(range(num_batches), disable=not verbose):
|
|
codes = autoregressive.inference_speech(
|
|
auto_conditioning,
|
|
text_tokens,
|
|
do_sample=True,
|
|
top_p=top_p,
|
|
temperature=temperature,
|
|
num_return_sequences=self.autoregressive_batch_size,
|
|
length_penalty=length_penalty,
|
|
repetition_penalty=repetition_penalty,
|
|
max_generate_length=max_mel_tokens,
|
|
**hf_generate_kwargs,
|
|
)
|
|
padding_needed = max_mel_tokens - codes.shape[1]
|
|
codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
|
|
samples.append(codes)
|
|
self.autoregressive_batch_size = orig_batch_size # in the case of single_sample
|
|
|
|
clip_results = []
|
|
with self.temporary_cuda(self.clvp) as clvp, torch.autocast(
|
|
device_type="cuda", dtype=torch.float16, enabled=half
|
|
):
|
|
for batch in tqdm(samples, disable=not verbose):
|
|
for i in range(batch.shape[0]):
|
|
batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
|
|
clvp_res = clvp(
|
|
text_tokens.repeat(batch.shape[0], 1),
|
|
batch,
|
|
return_loss=False,
|
|
)
|
|
clip_results.append(clvp_res)
|
|
|
|
clip_results = torch.cat(clip_results, dim=0)
|
|
samples = torch.cat(samples, dim=0)
|
|
best_results = samples[torch.topk(clip_results, k=k).indices]
|
|
del samples
|
|
|
|
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
|
|
# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
|
|
# results, but will increase memory usage.
|
|
with self.temporary_cuda(self.autoregressive) as autoregressive:
|
|
best_latents = autoregressive(
|
|
auto_conditioning.repeat(k, 1),
|
|
text_tokens.repeat(k, 1),
|
|
torch.tensor([text_tokens.shape[-1]], device=text_tokens.device),
|
|
best_results,
|
|
torch.tensor(
|
|
[best_results.shape[-1] * self.autoregressive.mel_length_compression],
|
|
device=text_tokens.device,
|
|
),
|
|
return_latent=True,
|
|
clip_inputs=False,
|
|
)
|
|
del auto_conditioning
|
|
|
|
if verbose:
|
|
print("Transforming autoregressive outputs into audio..")
|
|
wav_candidates = []
|
|
for b in range(best_results.shape[0]):
|
|
codes = best_results[b].unsqueeze(0)
|
|
latents = best_latents[b].unsqueeze(0)
|
|
|
|
# Find the first occurrence of the "calm" token and trim the codes to that.
|
|
ctokens = 0
|
|
for code in range(codes.shape[-1]):
|
|
if codes[0, code] == calm_token:
|
|
ctokens += 1
|
|
else:
|
|
ctokens = 0
|
|
if ctokens > 8: # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
|
|
latents = latents[:, :code]
|
|
break
|
|
with self.temporary_cuda(self.diffusion) as diffusion:
|
|
mel = do_spectrogram_diffusion(
|
|
diffusion,
|
|
diffuser,
|
|
latents,
|
|
diffusion_conditioning,
|
|
temperature=diffusion_temperature,
|
|
verbose=verbose,
|
|
)
|
|
with self.temporary_cuda(self.vocoder) as vocoder:
|
|
wav = vocoder.inference(mel)
|
|
wav_candidates.append(wav.cpu())
|
|
|
|
def potentially_redact(clip, text):
|
|
if self.enable_redaction:
|
|
return self.aligner.redact(clip.squeeze(1), text).unsqueeze(1)
|
|
return clip
|
|
|
|
wav_candidates = [potentially_redact(wav_candidate, text) for wav_candidate in wav_candidates]
|
|
|
|
if len(wav_candidates) > 1:
|
|
res = wav_candidates
|
|
else:
|
|
res = wav_candidates[0]
|
|
|
|
return_dict = {
|
|
"wav": res,
|
|
"deterministic_seed": None,
|
|
"text": None,
|
|
"voice_samples": None,
|
|
"conditioning_latents": None,
|
|
}
|
|
if return_deterministic_state:
|
|
return_dict = {
|
|
"wav": res,
|
|
"deterministic_seed": deterministic_seed,
|
|
"text": text,
|
|
"voice_samples": voice_samples,
|
|
"conditioning_latents": conditioning_latents,
|
|
}
|
|
return return_dict
|
|
|
|
def forward(self):
|
|
raise NotImplementedError("Tortoise Training is not implemented")
|
|
|
|
def eval_step(self):
|
|
raise NotImplementedError("Tortoise Training is not implemented")
|
|
|
|
def init_from_config(self):
|
|
raise NotImplementedError("Tortoise Training is not implemented")
|
|
|
|
def load_checkpoint(self):
|
|
raise NotImplementedError("Tortoise Training is not implemented")
|
|
|
|
def train_step(self):
|
|
raise NotImplementedError("Tortoise Training is not implemented")
|