diff --git a/api.py b/api.py index 6c3fb1e..7c33484 100644 --- a/api.py +++ b/api.py @@ -117,13 +117,14 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_ cond_mels.append(cond_mel) cond_mels = torch.stack(cond_mels, dim=1) - output_shape = (mel_codes.shape[0], 100, mel_codes.shape[-1]*4) - precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, False) + 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)[:,:,:mel_codes.shape[-1]*4] + return denormalize_tacotron_mel(mel)[:,:,:output_seq_len] class TextToSpeech: diff --git a/api_new_autoregressive.py b/api_new_autoregressive.py new file mode 100644 index 0000000..7a9d5ce --- /dev/null +++ b/api_new_autoregressive.py @@ -0,0 +1,245 @@ +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(512, 12).cpu().eval() + self.autoregressive.load_state_dict(torch.load('D:\\dlas\\experiments\\train_autoregressive_codegen\\models\\23000_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) \ No newline at end of file diff --git a/do_tts.py b/do_tts.py index af5c780..e48e9d5 100644 --- a/do_tts.py +++ b/do_tts.py @@ -5,7 +5,7 @@ import torch import torch.nn.functional as F import torchaudio -from api import TextToSpeech, load_conditioning +from api_new_autoregressive import TextToSpeech, load_conditioning from utils.audio import load_audio from utils.tokenizer import VoiceBpeTokenizer @@ -28,7 +28,7 @@ if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.") 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_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=32) parser.add_argument('-batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16) parser.add_argument('-num_diffusion_samples', type=int, help='Number of outputs that progress to the diffusion stage.', default=16) parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/') diff --git a/models/diffusion_decoder.py b/models/diffusion_decoder.py index cacdfc1..1baf809 100644 --- a/models/diffusion_decoder.py +++ b/models/diffusion_decoder.py @@ -212,7 +212,7 @@ class DiffusionTts(nn.Module): } return groups - def timestep_independent(self, aligned_conditioning, conditioning_input, return_code_pred): + def timestep_independent(self, aligned_conditioning, conditioning_input, expected_seq_len, return_code_pred): # Shuffle aligned_latent to BxCxS format if is_latent(aligned_conditioning): aligned_conditioning = aligned_conditioning.permute(0, 2, 1) @@ -227,7 +227,7 @@ class DiffusionTts(nn.Module): cond_emb = conds.mean(dim=-1) cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1) if is_latent(aligned_conditioning): - code_emb = self.latent_converter(aligned_conditioning) + code_emb = self.autoregressive_latent_converter(aligned_conditioning) else: code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1) code_emb = self.code_converter(code_emb) @@ -240,7 +240,7 @@ class DiffusionTts(nn.Module): device=code_emb.device) < self.unconditioned_percentage code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1), code_emb) - expanded_code_emb = F.interpolate(code_emb, size=aligned_conditioning.shape[-1]*4, mode='nearest') + expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest') if not return_code_pred: return expanded_code_emb @@ -250,7 +250,6 @@ class DiffusionTts(nn.Module): mel_pred = mel_pred * unconditioned_batches.logical_not() return expanded_code_emb, mel_pred - def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False): """ Apply the model to an input batch. @@ -275,11 +274,12 @@ class DiffusionTts(nn.Module): if precomputed_aligned_embeddings is not None: code_emb = precomputed_aligned_embeddings else: - code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, True) + code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, x.shape[-1], True) if is_latent(aligned_conditioning): unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters())) else: unused_params.extend(list(self.latent_converter.parameters())) + unused_params.append(self.unconditioned_embedding) time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) diff --git a/models/new_autoregressive.py b/models/new_autoregressive.py new file mode 100644 index 0000000..a6d8dee --- /dev/null +++ b/models/new_autoregressive.py @@ -0,0 +1,293 @@ +import functools + +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import GPT2PreTrainedModel, GPT2Config +from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions +from x_transformers import TransformerWrapper, Encoder, Decoder + +from models.arch_util import AttentionBlock + + +class InferenceModel(GPT2PreTrainedModel): + """ + Implementation of GPT2PreTrainedModel from transformers, which allows us to use their generation library with + this transformer. + """ + def __init__(self, model): + super().__init__(GPT2Config()) + self.transformer = model + self.context = None + + def parallelize(self, device_map=None): + # Not implemented. + pass + + def deparallelize(self): + # Not implemented. + pass + + def get_output_embeddings(self): + assert False, "Unsupported operation." + + def set_output_embeddings(self, new_embeddings): + assert False, "Unsupported operation." + + def store_context(self, context): + self.context = context + + def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + return { + "input_ids": input_ids, + "past_key_values": past, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + assert self.context is not None + assert inputs_embeds is None # Not supported by this inference model. + assert labels is None # Training not supported by this inference model. + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + hidden_states = self.transformer.decoder(input_ids, context=self.context, return_embeddings=True) + logits = self.transformer.decoder.transformer.to_logits(hidden_states) + + if not return_dict: + return (logits, ) + + return CausalLMOutputWithCrossAttentions( + loss=None, + logits=logits, + past_key_values=None, + hidden_states=hidden_states, + attentions=None, + cross_attentions=None, + ) + + @staticmethod + def _reorder_cache(past, beam_idx): + """ + This function is used to re-order the :obj:`past_key_values` cache if + :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is + called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step. + """ + return tuple( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) + for layer_past in past + ) + + +class ResBlock(nn.Module): + """ + Basic residual convolutional block that uses GroupNorm. + """ + def __init__(self, chan): + super().__init__() + self.net = nn.Sequential( + nn.Conv1d(chan, chan, kernel_size=3, padding=1), + nn.GroupNorm(chan//8, chan), + nn.ReLU(), + nn.Conv1d(chan, chan, kernel_size=3, padding=1), + nn.GroupNorm(chan//8, chan) + ) + + def forward(self, x): + return F.relu(self.net(x) + x) + + +class ConditioningEncoder(nn.Module): + def __init__(self, + spec_dim, + embedding_dim, + attn_blocks=6, + num_attn_heads=4, + do_checkpointing=False): + super().__init__() + attn = [] + self.init = nn.Sequential(nn.Conv1d(spec_dim, embedding_dim//4, kernel_size=5, padding=2), + nn.Conv1d(embedding_dim//4, embedding_dim//2, kernel_size=3, padding=1, stride=2), + ResBlock(embedding_dim//2), + nn.Conv1d(embedding_dim//2, embedding_dim, kernel_size=3, padding=1, stride=2)) + for a in range(attn_blocks): + attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing)) + self.attn = nn.Sequential(*attn) + self.dim = embedding_dim + + def forward(self, x): + h = self.init(x) + h = self.attn(h) + return h.mean(dim=2) + + +class CheckpointedLayer(nn.Module): + """ + Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses + checkpoint for all other args. + """ + def __init__(self, wrap): + super().__init__() + self.wrap = wrap + + def forward(self, x, *args, **kwargs): + for k, v in kwargs.items(): + assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing. + partial = functools.partial(self.wrap, **kwargs) + return torch.utils.checkpoint.checkpoint(partial, x, *args) + + +class CheckpointedXTransformerWrapper(nn.Module): + """ + Wraps a TransformerWrapper and applies CheckpointedLayer to each layer. + """ + def __init__(self, checkpoint=True, **xtransformer_kwargs): + super().__init__() + self.transformer = TransformerWrapper(**xtransformer_kwargs) + + if not checkpoint: + return + for i in range(len(self.transformer.attn_layers.layers)): + n, b, r = self.transformer.attn_layers.layers[i] + self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r]) + + def forward(self, x, **kwargs): + return self.transformer(x, **kwargs) + + +class AutoregressiveCodegen(nn.Module): + def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, max_text_tokens=4000, + max_mel_tokens=4000, dropout=.1): + super().__init__() + + self.START_TOKEN=8192 + self.STOP_TOKEN=8193 + self.max_mel_tokens = max_mel_tokens + self.minicoder = ConditioningEncoder(80, model_dim, do_checkpointing=False) + self.encoder = CheckpointedXTransformerWrapper( + num_tokens=num_text_tokens, + max_seq_len=max_text_tokens, + attn_layers = Encoder( + depth=depth//2, + heads=model_dim//64, + dim=model_dim, + attn_dropout=dropout, + ff_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + ff_mult=1, + rotary_pos_emb=True, + rel_pos_bias=True, + )) + self.decoder = CheckpointedXTransformerWrapper( + num_tokens=num_mel_tokens, + max_seq_len=max_mel_tokens, + attn_layers=Decoder( + depth=depth, + heads=model_dim//64, + dim=model_dim, + attn_dropout=dropout, + ff_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + ff_mult=1, + rotary_pos_emb=True, + rel_pos_bias=True, + cross_attend=True, + )) + + def get_grad_norm_parameter_groups(self): + return { + 'encoder': list(self.encoder.parameters()), + 'decoder': list(self.decoder.parameters()), + 'minicoder': list(self.minicoder.parameters()), + } + + def forward(self, text_codes, conditioning_signal, mel_codes, wav_lengths, return_loss=True): + # Format mel_codes with a stop token on the end. + mel_lengths = wav_lengths // 1024 + 1 + for b in range(mel_codes.shape[0]): + mel_codes[b, mel_lengths[b]:] = self.STOP_TOKEN + mel_codes = F.pad(mel_codes, (0, 1), value=self.STOP_TOKEN) + + # Build the context + if len(conditioning_signal.shape) != 4: + conditioning_signal = conditioning_signal.unsqueeze(1) + cond_embs = [] + for i in range(conditioning_signal.shape[1]): + cond_embs.append(self.minicoder(conditioning_signal[:, i])) + cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True) + enc_text = self.encoder(text_codes, return_embeddings=True) + context = torch.cat([cond_emb, enc_text], dim=1) + + # Execute the decoder + dec_inputs = F.pad(mel_codes, (1,0), value=self.START_TOKEN)[:, :-1] + dec = self.decoder(dec_inputs, context=context) + if not return_loss: + return dec + loss_mel = F.cross_entropy(dec.permute(0,2,1), mel_codes) + return loss_mel + + def generate(self, conditioning_signal, text_codes, **hf_generate_kwargs): + if not hasattr(self, 'inference_model'): + self.inference_model = InferenceModel(self) + + if len(conditioning_signal.shape) != 4: + conditioning_signal = conditioning_signal.unsqueeze(1) + cond_embs = [] + for i in range(conditioning_signal.shape[1]): + cond_embs.append(self.minicoder(conditioning_signal[:, i])) + cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True) + enc_text = self.encoder(text_codes, return_embeddings=True) + context = torch.cat([cond_emb, enc_text], dim=1) + self.inference_model.store_context(context) + + gen = self.inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN, + max_length=250, output_attentions=False, return_dict_in_generate=True, + **hf_generate_kwargs) + return gen.sequences + + +if __name__ == '__main__': + codegen = AutoregressiveCodegen(1024, 20) + codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200))) + codegen(torch.randint(0,256, (2,200)), + torch.randn(2,80,120), + torch.randint(0,8192, (2,350)), + torch.tensor([192,350])) \ No newline at end of file