clear out new_autoregressive api

api_new_autoregressive.py

@@ -1,245 +0,0 @@
-import argparse
-import os
-import random
-from urllib import request
-
-import torch
-import torch.nn.functional as F
-import torchaudio
-import progressbar
-import ocotillo
-
-from models.diffusion_decoder import DiffusionTts
-from models.autoregressive import UnifiedVoice
-from tqdm import tqdm
-
-from models.arch_util import TorchMelSpectrogram
-from models.new_autoregressive import AutoregressiveCodegen
-from models.text_voice_clip import VoiceCLIP
-from models.vocoder import UnivNetGenerator
-from utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel
-from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
-from utils.tokenizer import VoiceBpeTokenizer, lev_distance
-
-
-pbar = None
-def download_models():
-    MODELS = {
-        'clip.pth': 'https://huggingface.co/jbetker/tortoise-tts-clip/resolve/main/pytorch-model.bin',
-        'diffusion.pth': 'https://huggingface.co/jbetker/tortoise-tts-diffusion-v1/resolve/main/pytorch-model.bin',
-        'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-autoregressive/resolve/main/pytorch-model.bin'
-    }
-    os.makedirs('.models', exist_ok=True)
-    def show_progress(block_num, block_size, total_size):
-        global pbar
-        if pbar is None:
-            pbar = progressbar.ProgressBar(maxval=total_size)
-            pbar.start()
-
-        downloaded = block_num * block_size
-        if downloaded < total_size:
-            pbar.update(downloaded)
-        else:
-            pbar.finish()
-            pbar = None
-    for model_name, url in MODELS.items():
-        if os.path.exists(f'.models/{model_name}'):
-            continue
-        print(f'Downloading {model_name} from {url}...')
-        request.urlretrieve(url, f'.models/{model_name}', show_progress)
-        print('Done.')
-
-
-def pad_or_truncate(t, length):
-    if t.shape[-1] == length:
-        return t
-    elif t.shape[-1] < length:
-        return F.pad(t, (0, length-t.shape[-1]))
-    else:
-        return t[..., :length]
-
-
-def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1):
-    """
-    Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
-    """
-    return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
-                           model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps),
-                           conditioning_free=cond_free, conditioning_free_k=cond_free_k)
-
-
-def load_conditioning(clip, cond_length=132300):
-    gap = clip.shape[-1] - cond_length
-    if gap < 0:
-        clip = F.pad(clip, pad=(0, abs(gap)))
-    elif gap > 0:
-        rand_start = random.randint(0, gap)
-        clip = clip[:, rand_start:rand_start + cond_length]
-    mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0)
-    return mel_clip.unsqueeze(0).cuda()
-
-
-def fix_autoregressive_output(codes, stop_token):
-    """
-    This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
-    trained on and what the autoregressive code generator creates (which has no padding or end).
-    This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
-    a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
-    and copying out the last few codes.
-
-    Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
-    """
-    # Strip off the autoregressive stop token and add padding.
-    stop_token_indices = (codes == stop_token).nonzero()
-    if len(stop_token_indices) == 0:
-        print("No stop tokens found, enjoy that output of yours!")
-        return codes
-    else:
-        codes[stop_token_indices] = 83
-    stm = stop_token_indices.min().item()
-    codes[stm:] = 83
-    if stm - 3 < codes.shape[0]:
-        codes[-3] = 45
-        codes[-2] = 45
-        codes[-1] = 248
-
-    return codes
-
-
-def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_samples, temperature=1):
-    """
-    Uses the specified diffusion model to convert discrete codes into a spectrogram.
-    """
-    with torch.no_grad():
-        cond_mels = []
-        for sample in conditioning_samples:
-            sample = pad_or_truncate(sample, 102400)
-            cond_mel = wav_to_univnet_mel(sample.to(mel_codes.device), do_normalization=False)
-            cond_mels.append(cond_mel)
-        cond_mels = torch.stack(cond_mels, dim=1)
-
-        output_seq_len = mel_codes.shape[-1]*4*24000//22050  # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
-        output_shape = (mel_codes.shape[0], 100, output_seq_len)
-        precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, output_seq_len, False)
-
-        noise = torch.randn(output_shape, device=mel_codes.device) * temperature
-        mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
-                                      model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings})
-        return denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
-
-
-class TextToSpeech:
-    def __init__(self, autoregressive_batch_size=32):
-        self.autoregressive_batch_size = autoregressive_batch_size
-        self.tokenizer = VoiceBpeTokenizer()
-        download_models()
-
-        self.autoregressive = AutoregressiveCodegen(1024, 16).cpu().eval()
-        self.autoregressive.load_state_dict(torch.load('X:\\dlas\\experiments\\train_autoregressive_codegen\\models\\20750_codegen_ema.pth'))
-
-        self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12,
-                             text_seq_len=350, text_heads=8,
-                             num_speech_tokens=8192, speech_enc_depth=12, speech_heads=8, speech_seq_len=430,
-                             use_xformers=True).cpu().eval()
-        self.clip.load_state_dict(torch.load('.models/clip.pth'))
-
-        self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
-                                      in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
-                                      layer_drop=0, unconditioned_percentage=0).cpu().eval()
-        self.diffusion.load_state_dict(torch.load('.models/diffusion.pth'))
-
-        self.vocoder = UnivNetGenerator().cpu()
-        self.vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g'])
-        self.vocoder.eval(inference=True)
-
-    def tts(self, text, voice_samples, k=1,
-            # autoregressive generation parameters follow
-            num_autoregressive_samples=512, temperature=.5, length_penalty=2, repetition_penalty=2.0, top_p=.5,
-            typical_sampling=False, typical_mass=.9,
-            # diffusion generation parameters follow
-            diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=.7,):
-        text = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
-        text = F.pad(text, (0, 1))  # This may not be necessary.
-
-        conds = []
-        if not isinstance(voice_samples, list):
-            voice_samples = [voice_samples]
-        for vs in voice_samples:
-            conds.append(load_conditioning(vs))
-        conds = torch.stack(conds, dim=1)
-
-        diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
-
-        with torch.no_grad():
-            samples = []
-            num_batches = num_autoregressive_samples // self.autoregressive_batch_size
-            stop_mel_token = self.autoregressive.STOP_TOKEN
-            self.autoregressive = self.autoregressive.cuda()
-            for _ in tqdm(range(num_batches)):
-                codes = self.autoregressive.generate(conds, text,
-                                                     do_sample=True,
-                                                     top_p=top_p,
-                                                     temperature=temperature,
-                                                     num_return_sequences=self.autoregressive_batch_size,
-                                                     length_penalty=length_penalty,
-                                                     repetition_penalty=repetition_penalty,
-                                                     typical_sampling=typical_sampling,
-                                                     typical_mass=typical_mass)
-                padding_needed = 250 - codes.shape[1]
-                codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
-                samples.append(codes)
-            #self.autoregressive = self.autoregressive.cpu()
-
-            clip_results = []
-            self.clip = self.clip.cuda()
-            for batch in samples:
-                for i in range(batch.shape[0]):
-                    batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
-                bad_toks = batch >= 8192
-                batch = batch * bad_toks.logical_not()
-                clip_results.append(self.clip(text.repeat(batch.shape[0], 1), batch, return_loss=False))
-            clip_results = torch.cat(clip_results, dim=0)
-            samples = torch.cat(samples, dim=0)
-            best_results = samples[torch.topk(clip_results, k=k).indices]
-            self.clip = self.clip.cpu()
-            del samples
-
-            print("Performing vocoding..")
-            wav_candidates = []
-            self.diffusion = self.diffusion.cuda()
-            self.vocoder = self.vocoder.cuda()
-            for b in range(best_results.shape[0]):
-                code = best_results[b].unsqueeze(0)
-                mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, voice_samples, temperature=diffusion_temperature)
-                wav = self.vocoder.inference(mel)
-                wav_candidates.append(wav.cpu())
-            self.diffusion = self.diffusion.cpu()
-            self.vocoder = self.vocoder.cpu()
-
-            if len(wav_candidates) > 1:
-                return wav_candidates
-            return wav_candidates[0]
-
-    def refine_for_intellibility(self, wav_candidates, corresponding_codes, output_path):
-        """
-        Further refine the remaining candidates using a ASR model to pick out the ones that are the most understandable.
-        TODO: finish this function
-        :param wav_candidates:
-        :return:
-        """
-        transcriber = ocotillo.Transcriber(on_cuda=True)
-        transcriptions = transcriber.transcribe_batch(torch.cat(wav_candidates, dim=0).squeeze(1), 24000)
-        best = 99999999
-        for i, transcription in enumerate(transcriptions):
-            dist = lev_distance(transcription, args.text.lower())
-            if dist < best:
-                best = dist
-                best_codes = corresponding_codes[i].unsqueeze(0)
-                best_wav = wav_candidates[i]
-        del transcriber
-        torchaudio.save(os.path.join(output_path, f'{voice}_poor.wav'), best_wav.squeeze(0).cpu(), 24000)
-
-        # Perform diffusion again with the high-quality diffuser.
-        mel = do_spectrogram_diffusion(diffusion, final_diffuser, best_codes, cond_diffusion, mean=False)
-        wav = vocoder.inference(mel)
-        torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), wav.squeeze(0).cpu(), 24000)