From dc9cd8c206b494d6b0cea9bcc4be27aac792eb33 Mon Sep 17 00:00:00 2001 From: James Betker Date: Tue, 18 Jan 2022 21:14:17 -0700 Subject: [PATCH] Update use_gpt_tts to be usable with unified_voice2 --- .../models/gpt_voice/transformer_builders.py | 3 + codes/models/gpt_voice/unified_voice2.py | 168 +++++++++++++++++- codes/scripts/audio/gen/use_gpt_tts.py | 31 ++-- 3 files changed, 182 insertions(+), 20 deletions(-) diff --git a/codes/models/gpt_voice/transformer_builders.py b/codes/models/gpt_voice/transformer_builders.py index ae10d714..d117c932 100644 --- a/codes/models/gpt_voice/transformer_builders.py +++ b/codes/models/gpt_voice/transformer_builders.py @@ -42,6 +42,9 @@ class LearnedPositionEmbeddings(nn.Module): sl = x.shape[1] return self.emb(torch.arange(0, sl, device=x.device)) + def get_fixed_embedding(self, ind, dev): + return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0) + def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing): """ diff --git a/codes/models/gpt_voice/unified_voice2.py b/codes/models/gpt_voice/unified_voice2.py index 726ef167..69b61fb7 100644 --- a/codes/models/gpt_voice/unified_voice2.py +++ b/codes/models/gpt_voice/unified_voice2.py @@ -3,10 +3,11 @@ import functools import torch import torch.nn as nn import torch.nn.functional as F -from transformers import GPT2Model, GPT2Config +from transformers import GPT2Model, GPT2Config, GPT2PreTrainedModel +from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions +from transformers.utils.model_parallel_utils import get_device_map, assert_device_map from models.arch_util import AttentionBlock -from models.gpt_voice.gpt_asr_hf import GPT2InferenceModel from models.gpt_voice.gpt_asr_hf2 import ResBlock from models.gpt_voice.transformer_builders import build_hf_gpt_transformer from models.tacotron2.text import symbols @@ -14,6 +15,160 @@ from trainer.networks import register_model from utils.util import opt_get +class GPT2InferenceModel(GPT2PreTrainedModel): + def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear): + super().__init__(config) + self.transformer = gpt + self.text_pos_embedding = text_pos_emb + self.embeddings = embeddings + self.lm_head = nn.Sequential(norm, linear) + + # Model parallel + self.model_parallel = False + self.device_map = None + self.cached_mel_emb = None + + def parallelize(self, device_map=None): + self.device_map = ( + get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) + if device_map is None + else device_map + ) + assert_device_map(self.device_map, len(self.transformer.h)) + self.transformer.parallelize(self.device_map) + self.lm_head = self.lm_head.to(self.transformer.first_device) + self.model_parallel = True + + def deparallelize(self): + self.transformer.deparallelize() + self.transformer = self.transformer.to("cpu") + self.lm_head = self.lm_head.to("cpu") + self.model_parallel = False + torch.cuda.empty_cache() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def store_mel_emb(self, mel_emb): + self.cached_mel_emb = mel_emb + + 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.cached_mel_emb 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 + + # Create embedding + mel_len = self.cached_mel_emb.shape[1] + if input_ids.shape[1] != 1: + text_inputs = input_ids[:, mel_len:] + text_emb = self.embeddings(text_inputs) + text_emb = text_emb + self.text_pos_embedding(text_emb) + if self.cached_mel_emb.shape[0] != text_emb.shape[0]: + mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0) + else: + mel_emb = self.cached_mel_emb + emb = torch.cat([mel_emb, text_emb], dim=1) + else: + emb = self.embeddings(input_ids) + emb = emb + self.text_pos_embedding.get_fixed_embedding(attention_mask.shape[1]-mel_len, attention_mask.device) + + transformer_outputs = self.transformer( + inputs_embeds=emb, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + # Set device for model parallelism + if self.model_parallel: + torch.cuda.set_device(self.transformer.first_device) + hidden_states = hidden_states.to(self.lm_head.weight.device) + + lm_logits = self.lm_head(hidden_states) + + if not return_dict: + return (lm_logits,) + transformer_outputs[1:] + + return CausalLMOutputWithCrossAttentions( + loss=None, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + cross_attentions=transformer_outputs.cross_attentions, + ) + + @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 ConditioningEncoder(nn.Module): def __init__(self, spec_dim, @@ -275,9 +430,9 @@ class UnifiedVoice(nn.Module): return loss_mel.mean() def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs): + seq_length = self.max_mel_tokens + self.max_text_tokens + 2 if not hasattr(self, 'inference_model'): # TODO: Decouple gpt_config from this inference model. - seq_length = self.max_mel_tokens + self.max_text_tokens + 5 gpt_config = GPT2Config(vocab_size=self.max_mel_tokens, n_positions=seq_length, n_ctx=seq_length, @@ -286,7 +441,8 @@ class UnifiedVoice(nn.Module): n_head=self.heads, gradient_checkpointing=False, use_cache=True) - self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.final_norm, self.mel_head) + self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head) + self.gpt.wte = self.mel_embedding text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token) text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token) @@ -301,11 +457,11 @@ class UnifiedVoice(nn.Module): emb = torch.cat([conds, text_emb], dim=1) self.inference_model.store_mel_emb(emb) - fake_inputs = torch.full((emb.shape[0], emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device) + fake_inputs = torch.full((emb.shape[0], conds.shape[1]+emb.shape[1],), fill_value=1, dtype=torch.long, device=text_inputs.device) fake_inputs[:,-1] = self.start_mel_token gen = self.inference_model.generate(fake_inputs, bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token, eos_token_id=self.stop_mel_token, - max_length=self.seq_length, **hf_generate_kwargs) + max_length=seq_length, **hf_generate_kwargs) return gen[:, fake_inputs.shape[1]:] diff --git a/codes/scripts/audio/gen/use_gpt_tts.py b/codes/scripts/audio/gen/use_gpt_tts.py index a7a2746e..49ff364f 100644 --- a/codes/scripts/audio/gen/use_gpt_tts.py +++ b/codes/scripts/audio/gen/use_gpt_tts.py @@ -80,13 +80,13 @@ def fix_autoregressive_output(codes, stop_token): if __name__ == '__main__': preselected_cond_voices = { - 'trump': 'D:\\data\\audio\\sample_voices\\trump.wav', - 'ryan_reynolds': 'D:\\data\\audio\\sample_voices\\ryan_reynolds.wav', - 'ed_sheeran': 'D:\\data\\audio\\sample_voices\\ed_sheeran.wav', - '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', - 'dan_carlin': 'Y:\\clips\\books1\\5_dchha06 Shield of the West\\00476.wav', - 'libri_test': 'Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav' + 'trump': ['D:\\data\\audio\\sample_voices\\trump.wav'], + 'ryan_reynolds': ['D:\\data\\audio\\sample_voices\\ryan_reynolds.wav'], + 'ed_sheeran': ['D:\\data\\audio\\sample_voices\\ed_sheeran.wav'], + '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'], + 'libri_test': ['Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav'] } parser = argparse.ArgumentParser() @@ -94,17 +94,16 @@ if __name__ == '__main__': parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator') parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\6100_generator_ema.pth') parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae') - parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts_unified\\train_gpt_tts_unified.yml') + parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts_unified.yml') parser.add_argument('-gpt_tts_model_name', type=str, help='Name of the GPT TTS model in opt.', default='gpt') - parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts_unified\\models\\60000_gpt_ema.pth') + parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts_unified_large\\models\\40000_gpt_ema.pth') parser.add_argument('-opt_clip', type=str, help='Path to options YAML file used to train the CLIP model', default='X:\\dlas\\experiments\\train_clip_text_to_voice.yml') parser.add_argument('-clip_model_name', type=str, help='Name of the CLIP model in opt.', default='clip') parser.add_argument('-clip_model_path', type=str, help='CLIP model checkpoint to load.', default='X:\\dlas\\experiments\\train_clip_text_to_voice_masking_bigger_batch\\models\\23500_clip_ema.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_path', type=str, help='Path to condioning sample.', default='') parser.add_argument('-cond_preset', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='libri_test') parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=128) - parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=2) + parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=8) 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/use_gpt_tts') args = parser.parse_args() @@ -115,7 +114,7 @@ if __name__ == '__main__': with open(args.opt_gpt_tts, mode='r') as f: gpt_opt = yaml.load(f, Loader=Loader) gpt_opt['networks'][args.gpt_tts_model_name]['kwargs']['checkpointing'] = False # Required for beam search - gpt = load_model_from_config(preloaded_options=gpt_opt, model_name=args.gpt_tts_model_name, also_load_savepoint=False, load_path=args.gpt_tts_model_path, strict_load=False).eval() + gpt = load_model_from_config(preloaded_options=gpt_opt, model_name=args.gpt_tts_model_name, also_load_savepoint=False, load_path=args.gpt_tts_model_path).eval() stop_mel_token = gpt.stop_mel_token print("Loading data..") @@ -123,8 +122,12 @@ if __name__ == '__main__': text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda() text = F.pad(text, (0,1)) # This may not be necessary. - cond_path = args.cond_path if args.cond_preset is None else preselected_cond_voices[args.cond_preset] - conds, cond_wav = load_conditioning(cond_path, cond_length=88000) + 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. with torch.no_grad(): print("Performing GPT inference..")