From 1a2fb5db63fcfe83857ad27d5707601d8ecd8364 Mon Sep 17 00:00:00 2001 From: James Betker Date: Thu, 3 Feb 2022 22:18:21 -0700 Subject: [PATCH] Update docs --- README.md | 8 ++- do_tts.py | 184 +++++++++++++++++++++++++++---------------------- utils/audio.py | 2 + 3 files changed, 110 insertions(+), 84 deletions(-) diff --git a/README.md b/README.md index b9e7d87..61dd065 100644 --- a/README.md +++ b/README.md @@ -14,19 +14,25 @@ expect ~5 seconds of speech to take ~30 seconds to produce on the latest hardwar ## What the heck is this? -Tortoise TTS is inspired by OpenAI's DALLE, applied to speech data. It is made up of 4 separate models that work together: +Tortoise TTS is inspired by OpenAI's DALLE, applied to speech data. It is made up of 4 separate models that work together. +These models are all derived from different repositories which are all linked. All the models have been modified +for this use case (some substantially so). First, an autoregressive transformer stack predicts discrete speech "tokens" given a text prompt. This model is very similar to the GPT model used by DALLE, except it operates on speech data. +Based on: [GPT2 from Transformers](https://huggingface.co/docs/transformers/model_doc/gpt2) Next, a CLIP model judges a batch of outputs from the autoregressive transformer against the provided text and stack ranks the outputs according to most probable. You could use greedy or beam-search decoding but in my experience CLIP decoding creates considerably better results. +Based on [CLIP from lucidrains](https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py) Next, the speech "tokens" are decoded into a low-quality MEL spectrogram using a VQVAE. +Based on [VQVAE2 by rosinality](https://github.com/rosinality/vq-vae-2-pytorch) Finally, the output of the VQVAE is further decoded by a UNet diffusion model into raw audio, which can be placed in a wav file. +Based on [ImprovedDiffusion by openai](https://github.com/openai/improved-diffusion) ## How do I use this? diff --git a/do_tts.py b/do_tts.py index a508248..921c752 100644 --- a/do_tts.py +++ b/do_tts.py @@ -25,25 +25,7 @@ def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusi model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps)) -def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128): - """ - Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip. - """ - with torch.no_grad(): - mel = dvae_model.decode(mel_codes)[0] - - # Pad MEL to multiples of 2048//spectrogram_compression_factor - msl = mel.shape[-1] - dsl = 2048 // spectrogram_compression_factor - gap = dsl - (msl % dsl) - if gap > 0: - mel = torch.nn.functional.pad(mel, (0, gap)) - - output_shape = (mel.shape[0], 1, mel.shape[-1] * spectrogram_compression_factor) - return diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input}) - - -def load_conditioning(path, sample_rate=22050, cond_length=44100): +def load_conditioning(path, sample_rate=22050, cond_length=132300): rel_clip = load_audio(path, sample_rate) gap = rel_clip.shape[-1] - cond_length if gap < 0: @@ -82,86 +64,122 @@ def fix_autoregressive_output(codes, stop_token): return codes +def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, mean=False): + """ + Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip. + """ + with torch.no_grad(): + mel = dvae_model.decode(mel_codes)[0] + + # Pad MEL to multiples of 2048//spectrogram_compression_factor + msl = mel.shape[-1] + dsl = 2048 // spectrogram_compression_factor + gap = dsl - (msl % dsl) + if gap > 0: + mel = torch.nn.functional.pad(mel, (0, gap)) + + output_shape = (mel.shape[0], 1, mel.shape[-1] * spectrogram_compression_factor) + if mean: + return diffuser.p_sample_loop(diffusion_model, output_shape, noise=torch.zeros(output_shape, device=mel_codes.device), + model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input}) + else: + return diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input}) + + if __name__ == '__main__': + # These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing + # has shown that the model does not generalize to new voices very well. preselected_cond_voices = { - 'simmons': ['Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav'], - 'news_girl': ['Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00022.wav', 'Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00016.wav'], - 'dan_carlin': ['Y:\\clips\\books1\\5_dchha06 Shield of the West\\00476.wav', 'Y:\\clips\\books1\\15_dchha16 Nazi Tidbits\\00036.wav'], - 'libri_test': ['Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav'], + # Male voices + 'dotrice': ['voices/dotrice/1.wav', 'voices/dotrice/2.wav'], + 'harris': ['voices/male_harris1.wav', 'voices/male_harris2.wav'], + 'lescault': ['voices/male_lescault1.wav', 'voices/male_lescault2.wav'], + 'otto': ['voices/male_otto1.wav', 'voices/male_otto2.wav'], + # Female voices + 'atkins': ['voices/female_atkins1.wav', 'voices/female_atkins2.wav'], + 'grace': ['voices/female_grace1.wav', 'voices/female_grace2.wav'], + 'kennard': ['voices/female_kennard1.wav', 'voices/female_kennard2.wav'], + 'mol': ['voices/female_mol1.wav', 'voices/female_mol2.wav'], } parser = argparse.ArgumentParser() parser.add_argument('-autoregressive_model_path', type=str, help='Autoregressive model checkpoint to load.', default='.models/unified_voice.pth') parser.add_argument('-clip_model_path', type=str, help='CLIP model checkpoint to load.', default='.models/clip.pth') - parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='./models/diffusion_vocoder.pth') - parser.add_argument('-dvae_model_path', type=str, help='DVAE model checkpoint to load.', default='./models/dvae.pth') + parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='.models/diffusion_vocoder.pth') + parser.add_argument('-dvae_model_path', type=str, help='DVAE model checkpoint to load.', default='.models/dvae.pth') parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.") - parser.add_argument('-cond_preset', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dan_carlin') - parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=32) - parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=2) + parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dotrice,harris,lescault,otto,atkins,grace,kennard,mol') + parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=512) + parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=16) parser.add_argument('-num_outputs', type=int, help='Number of outputs to produce.', default=2) parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/') args = parser.parse_args() os.makedirs(args.output_path, exist_ok=True) - print("Loading GPT TTS..") - autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024, heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False).eval() - autoregressive.load_state_dict(torch.load(args.autoregressive_model_path)) - stop_mel_token = autoregressive.stop_mel_token + for voice in args.voice.split(','): + print("Loading GPT TTS..") + autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024, + heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False).cuda().eval() + autoregressive.load_state_dict(torch.load(args.autoregressive_model_path)) + stop_mel_token = autoregressive.stop_mel_token - print("Loading data..") - tokenizer = VoiceBpeTokenizer() - text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda() - text = F.pad(text, (0,1)) # This may not be necessary. - cond_paths = preselected_cond_voices[args.cond_preset] - conds = [] - for cond_path in cond_paths: - c, cond_wav = load_conditioning(cond_path, cond_length=132300) - conds.append(c) - conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model. + print("Loading data..") + tokenizer = VoiceBpeTokenizer() + text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda() + text = F.pad(text, (0,1)) # This may not be necessary. + cond_paths = preselected_cond_voices[voice] + conds = [] + for cond_path in cond_paths: + c, cond_wav = load_conditioning(cond_path) + conds.append(c) + conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model. - with torch.no_grad(): - print("Performing GPT inference..") - samples = [] - for b in tqdm(range(args.num_batches)): - codes = autoregressive.inference_speech(conds, text, num_beams=1, repetition_penalty=1.0, do_sample=True, top_k=50, top_p=.95, - temperature=.9, num_return_sequences=args.num_samples//args.num_batches, length_penalty=1) - padding_needed = 250 - codes.shape[1] - codes = F.pad(codes, (0, padding_needed), value=stop_mel_token) - samples.append(codes) - samples = torch.cat(samples, dim=0) - del autoregressive + with torch.no_grad(): + print("Performing autoregressive inference..") + samples = [] + for b in tqdm(range(args.num_batches)): + codes = autoregressive.inference_speech(conds, text, num_beams=1, repetition_penalty=1.0, do_sample=True, top_k=50, top_p=.95, + temperature=.9, num_return_sequences=args.num_samples//args.num_batches, length_penalty=1) + padding_needed = 250 - codes.shape[1] + codes = F.pad(codes, (0, padding_needed), value=stop_mel_token) + samples.append(codes) + del autoregressive - print("Loading CLIP..") - clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=8, text_seq_len=120, text_heads=8, - num_speech_tokens=8192, speech_enc_depth=10, speech_heads=8, speech_seq_len=250).eval() - clip.load_state_dict(torch.load(args.clip_model_path)) - print("Performing CLIP filtering..") - for i in range(samples.shape[0]): - samples[i] = fix_autoregressive_output(samples[i], stop_mel_token) - clip_results = clip(text.repeat(samples.shape[0], 1), - torch.full((samples.shape[0],), fill_value=text.shape[1]-1, dtype=torch.long, device='cuda'), - samples, torch.full((samples.shape[0],), fill_value=samples.shape[1]*1024, dtype=torch.long, device='cuda'), - return_loss=False) - best_results = samples[torch.topk(clip_results, k=args.num_outputs).indices] + print("Loading CLIP..") + clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=8, text_seq_len=120, text_heads=8, + num_speech_tokens=8192, speech_enc_depth=10, speech_heads=8, speech_seq_len=250).cuda().eval() + clip.load_state_dict(torch.load(args.clip_model_path)) + print("Performing CLIP filtering..") + clip_results = [] + for batch in samples: + for i in range(batch.shape[0]): + batch[i] = fix_autoregressive_output(batch[i], stop_mel_token) + text = text[:, :120] # Ugly hack to fix the fact that I didn't train CLIP to handle long enough text. + clip_results.append(clip(text.repeat(batch.shape[0], 1), + torch.full((batch.shape[0],), fill_value=text.shape[1]-1, dtype=torch.long, device='cuda'), + batch, torch.full((batch.shape[0],), fill_value=batch.shape[1]*1024, dtype=torch.long, device='cuda'), + 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=args.num_outputs).indices] - # Delete the autoregressive and clip models to free up GPU memory - del samples, clip + # Delete the autoregressive and clip models to free up GPU memory + del samples, clip - print("Loading DVAE..") - dvae = DiscreteVAE(positional_dims=1, channels=80, hidden_dim=512, num_resnet_blocks=3, codebook_dim=512, num_tokens=8192, num_layers=2, - record_codes=True, kernel_size=3, use_transposed_convs=False).eval() - dvae.load_state_dict(torch.load(args.dvae_model_path)) - print("Loading Diffusion Model..") - diffusion = DiscreteDiffusionVocoder(model_channels=128, dvae_dim=80, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1], - spectrogram_conditioning_resolutions=[2,512], attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2, - conditioning_inputs_provided=True, time_embed_dim_multiplier=4).eval() - diffusion.load_state_dict(torch.load(args.diffusion_model_path)) - diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=100) + print("Loading DVAE..") + dvae = DiscreteVAE(positional_dims=1, channels=80, hidden_dim=512, num_resnet_blocks=3, codebook_dim=512, num_tokens=8192, num_layers=2, + record_codes=True, kernel_size=3, use_transposed_convs=False).cuda().eval() + dvae.load_state_dict(torch.load(args.dvae_model_path)) + print("Loading Diffusion Model..") + diffusion = DiscreteDiffusionVocoder(model_channels=128, dvae_dim=80, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1], + spectrogram_conditioning_resolutions=[2,512], attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2, + conditioning_inputs_provided=True, time_embed_dim_multiplier=4).cuda().eval() + diffusion.load_state_dict(torch.load(args.diffusion_model_path)) + diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=100) - print("Performing vocoding..") - # Perform vocoding on each batch element separately: Vocoding is very memory (and compute!) intensive. - for b in range(best_results.shape[0]): - code = best_results[b].unsqueeze(0) - wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, code, cond_wav, spectrogram_compression_factor=256) - torchaudio.save(os.path.join(args.output_path, f'gpt_tts_output_{b}.wav'), wav.squeeze(0).cpu(), 22050) + print("Performing vocoding..") + # Perform vocoding on each batch element separately: The diffusion model is very memory (and compute!) intensive. + for b in range(best_results.shape[0]): + code = best_results[b].unsqueeze(0) + wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, code, cond_wav, spectrogram_compression_factor=256, mean=True) + torchaudio.save(os.path.join(args.output_path, f'{voice}_{b}.wav'), wav.squeeze(0).cpu(), 22050) diff --git a/utils/audio.py b/utils/audio.py index 5a61b25..22a2506 100644 --- a/utils/audio.py +++ b/utils/audio.py @@ -1,5 +1,7 @@ import torch import torchaudio +import numpy as np +from scipy.io.wavfile import read def load_wav_to_torch(full_path):