diff --git a/codes/models/gpt_voice/unified_voice2.py b/codes/models/gpt_voice/unified_voice2.py index a9063c8c..3b8d57b2 100644 --- a/codes/models/gpt_voice/unified_voice2.py +++ b/codes/models/gpt_voice/unified_voice2.py @@ -451,7 +451,7 @@ class UnifiedVoice(nn.Module): loss_mel = F.cross_entropy(mel_logits, mel_targets.long()) return loss_mel.mean() - def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs): + def inference_speech(self, speech_conditioning_input, text_inputs, return_attentions=False, **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. @@ -483,8 +483,27 @@ class UnifiedVoice(nn.Module): 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=seq_length, **hf_generate_kwargs) - return gen[:, fake_inputs.shape[1]:] + max_length=seq_length, output_attentions=return_attentions, return_dict_in_generate=True, **hf_generate_kwargs) + if return_attentions: + return gen.sequences[:, fake_inputs.shape[1]:], gen.attentions + else: + return gen.sequences[:, fake_inputs.shape[1]:] + + def convert_attentions_to_aligned_codes(self, text, attentions, codes, num_conds): + text_padding = num_conds+1 + num_text = text.shape[-1] + results = torch.empty_like(codes) + for t, att_tok in enumerate(attentions): + combined_attention_weights = torch.zeros((codes.shape[0], num_text), device=codes.device) + for lyr in att_tok: + token_to_text_attentions = lyr[:, :, -1, text_padding:(text_padding + num_text)].sum(dim=1) + combined_attention_weights = combined_attention_weights + token_to_text_attentions + break + most_attended_text_token = combined_attention_weights.argmax(dim=-1) + results[:, t] = most_attended_text_token + eos_token_mask = (codes != self.stop_mel_token) + return results * eos_token_mask + @register_model @@ -493,6 +512,10 @@ def register_unified_voice2(opt_net, opt): if __name__ == '__main__': + ld = torch.load('attentions.pth') + gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4) + gpt.convert_attentions_to_aligned_codes(*ld) + ''' gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4) l = gpt(torch.randn(2, 3, 80, 800), torch.randint(high=len(symbols), size=(2,120)), @@ -500,3 +523,4 @@ if __name__ == '__main__': torch.randint(high=8192, size=(2,250)), torch.tensor([250*256,195*256])) gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80])) + ''' diff --git a/codes/scripts/audio/gen/use_diffuse_tts.py b/codes/scripts/audio/gen/use_diffuse_tts.py index 4d787a94..b667bcf0 100644 --- a/codes/scripts/audio/gen/use_diffuse_tts.py +++ b/codes/scripts/audio/gen/use_diffuse_tts.py @@ -45,86 +45,6 @@ if __name__ == '__main__': 'adrift': 'Y:\\clips\\books2\\5608_Gear__W_Michael_-_Donovan_1-5_(2018-2021)_(book_4_Gear__W_Michael_-_Donovan_5_-_Adrift_(2021)_Gear__W_Michael_-_Adrift_(Donovan_5)_—_82__000000000\\00019.wav', } - provided_codes = [ - # but facts within easy reach of any one who cares to know them go to say that the greater abstenence of women is in some part - # due to an imperative conventionality and this conventionality is in a general way strongest were the patriarchal tradition - # the tradition that the woman is a chattel has retained its hold in greatest vigor - # 3570/5694/3570_5694_000008_000001.wav - [0, 0, 24, 0, 16, 0, 6, 0, 4, 0, 0, 0, 0, 0, 20, 0, 7, 0, 0, 19, 19, 0, 0, 6, 0, 0, 12, 12, 0, 4, 4, 0, 18, 18, - 0, 10, 0, 6, 11, 11, 10, 10, 9, 9, 4, 4, 4, 5, 5, 0, 7, 0, 0, 0, 0, 12, 0, 22, 22, 0, 4, 4, 0, 13, 13, 5, 0, 7, - 7, 0, 0, 19, 11, 0, 4, 4, 8, 20, 4, 4, 4, 7, 0, 9, 9, 0, 22, 4, 4, 0, 8, 0, 9, 5, 4, 4, 18, 11, 11, 8, 4, 4, 0, - 0, 0, 19, 19, 7, 0, 0, 13, 5, 5, 0, 12, 12, 4, 4, 6, 6, 8, 8, 4, 4, 0, 26, 9, 9, 8, 0, 18, 0, 0, 4, 4, 6, 6, - 11, 5, 0, 17, 17, 0, 0, 4, 4, 4, 4, 0, 0, 0, 21, 0, 8, 0, 0, 0, 0, 4, 4, 6, 6, 8, 0, 4, 4, 0, 0, 12, 0, 7, 7, - 0, 0, 22, 0, 4, 4, 6, 11, 11, 7, 6, 6, 4, 4, 6, 11, 5, 4, 4, 4, 0, 21, 0, 13, 5, 5, 7, 7, 0, 0, 6, 6, 5, 0, 13, - 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# from arcaic times down through all the length of the patriarchal regime it has been the office of the women to - # prepare and administer these luxuries and it has been the perquisite of the men of gentle birth and breeding - # to consume them - # 3570/5694/3570_5694_000007_000003.wav - [0, 0, 0, 0, 0, 0, 20, 13, 8, 0, 17, 0, 4, 4, 0, 7, 0, 13, 0, 0, 0, 0, 0, 19, 0, 0, 0, 7, 0, 0, 0, 0, 10, 0, 19, 0, 0, 0, 4, 4, 0, 0, 0, 0, 6, 0, 0, 0, 10, 0, 0, 17, 5, 0, 0, 0, 12, 0, 4, 0, 0, 0, 0, 14, 0, 0, 8, 0, 18, 0, 0, 0, 9, 0, 0, 0, 0, 4, 4, 0, 0, 0, 6, 11, 13, 8, 0, 16, 21, 21, 11, 0, 4, 4, 7, 0, 15, 0, 15, 15, 4, 4, 6, 11, 5, 5, 4, 4, 0, 15, 0, 5, 0, 0, 9, 9, 0, 21, 0, 0, 6, 11, 0, 4, 4, 8, 8, 20, 4, 4, 4, 6, 11, 5, 4, 4, 0, 0, 0, 23, 0, 7, 7, 0, 0, 0, 0, 0, 6, 6, 13, 13, 13, 10, 0, 0, 0, 0, 0, 7, 13, 13, 0, 19, 11, 11, 11, 0, 0, 7, 15, 15, 0, 4, 4, 4, 13, 13, 5, 0, 0, 0, 0, 21, 21, 0, 0, 10, 0, 0, 0, 0, 17, 5, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 6, 4, 4, 0, 0, 11, 7, 7, 0, 0, 12, 0, 4, 4, 0, 24, 5, 0, 0, 5, 5, 9, 0, 4, 6, 6, 11, 5, 4, 4, 0, 0, 8, 0, 20, 0, 0, 0, 20, 0, 10, 0, 0, 0, 19, 5, 0, 4, 4, 8, 0, 20, 4, 4, 6, 11, 5, 4, 4, 4, 18, 8, 0, 0, 0, 17, 5, 0, 9, 9, 0, 0, 4, 4, 0, 6, 6, 8, 0, 0, 4, 4, 0, 23, 23, 13, 5, 5, 0, 0, 0, 0, 23, 23, 0, 7, 0, 0, 0, 13, 5, 0, 0, 0, 4, 4, 0, 7, 0, 9, 14, 0, 4, 4, 0, 0, 7, 0, 14, 0, 0, 0, 17, 17, 10, 0, 9, 0, 10, 10, 0, 0, 12, 12, 0, 0, 0, 6, 0, 5, 13, 13, 0, 0, 0, 0, 4, 4, 4, 6, 11, 11, 5, 0, 0, 0, 12, 5, 5, 4, 4, 15, 15, 0, 16, 0, 0, 0, 28, 0, 0, 0, 16, 0, 0, 13, 13, 10, 0, 5, 5, 0, 0, 12, 12, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 0, 14, 4, 4, 10, 0, 6, 4, 4, 0, 11, 11, 7, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 0, 24, 5, 0, 0, 5, 5, 9, 9, 4, 4, 4, 6, 11, 5, 4, 4, 0, 0, 0, 23, 0, 5, 0, 13, 0, 0, 0, 0, 0, 30, 30, 16, 10, 10, 0, 0, 0, 12, 0, 10, 0, 0, 6, 5, 0, 4, 4, 8, 20, 0, 4, 4, 6, 11, 5, 4, 4, 0, 17, 5, 0, 0, 0, 9, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 20, 4, 4, 4, 0, 0, 21, 0, 5, 5, 0, 9, 9, 0, 0, 0, 6, 0, 15, 0, 5, 0, 4, 0, 0, 0, 24, 0, 10, 0, 13, 0, 0, 0, 0, 6, 11, 0, 0, 4, 0, 0, 7, 0, 9, 14, 14, 4, 4, 4, 0, 0, 24, 13, 5, 0, 0, 0, 5, 0, 0, 14, 10, 0, 9, 21, 21, 0, 4, 4, 0, 6, 8, 0, 4, 4, 0, 19, 8, 0, 9, 0, 0, 0, 0, 0, 0, 0, 12, 0, 16, 0, 17, 5, 0, 0, 4, 4, 6, 11, 5, 0, 17, 0, 4, 4, 4, 4, 0, 0], - # yes it is perfection she declared - # 1284/1180/1284_1180_000036_000000.wav - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 4, 4, 4, 4, 0, 0, 10, 0, 6, 0, 4, 4, 0, 0, 10, 0, 0, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 23, 0, 5, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 5, 0, 0, 0, 19, 0, 0, 6, 6, 0, 10, 0, 8, 0, 9, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 12, 11, 11, 5, 0, 4, 4, 0, 14, 0, 5, 0, 0, 0, 0, 19, 15, 15, 0, 0, 7, 0, 0, 0, 13, 0, 5, 0, 14, 4, 4, 4, 4, 0, 0, 0], - # then it must be somewhere in the blue forest - # 1284/1180/1284_1180_000016_000002.wav - [0, 0, 0, 6, 11, 5, 0, 9, 0, 4, 4, 10, 6, 4, 4, 0, 17, 17, 16, 0, 0, 12, 0, 6, 4, 4, 0, 24, 5, 5, 0, 0, 4, 4, 0, 0, 12, 12, 0, 8, 0, 0, 17, 5, 5, 0, 0, 18, 18, 11, 5, 0, 13, 13, 5, 0, 4, 4, 10, 9, 4, 4, 6, 11, 5, 4, 4, 0, 24, 15, 15, 16, 16, 0, 5, 5, 0, 0, 4, 4, 0, 0, 0, 20, 8, 8, 8, 0, 0, 0, 13, 13, 0, 5, 5, 0, 0, 0, 0, 0, 12, 12, 0, 0, 6, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0], - # happy youth that is ready to pack its valus and start for cathay on an hour's notice - # 4970/29093/4970_29093_000044_000002.wav - [0, 0, 0, 0, 11, 0, 7, 23, 0, 0, 0, 0, 23, 0, 22, 22, 0, 0, 0, 4, 4, 0, 0, 22, 8, 8, 16, 16, 0, 0, 0, 6, 6, 11, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 11, 7, 6, 0, 4, 4, 10, 0, 0, 12, 0, 4, 0, 13, 13, 5, 0, 7, 0, 0, 14, 22, 0, 0, 0, 4, 0, 6, 0, 8, 4, 4, 0, 0, 0, 0, 0, 0, 23, 0, 7, 0, 0, 19, 0, 0, 26, 4, 4, 4, 10, 0, 6, 0, 12, 4, 4, 0, 0, 0, 25, 0, 7, 0, 0, 0, 15, 0, 0, 16, 0, 0, 0, 0, 12, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 0, 14, 4, 4, 0, 12, 12, 0, 6, 0, 7, 0, 13, 0, 0, 0, 6, 0, 0, 4, 4, 0, 0, 0, 0, 20, 8, 0, 13, 0, 4, 4, 4, 0, 0, 19, 0, 7, 7, 0, 0, 0, 0, 0, 6, 11, 0, 0, 7, 0, 0, 0, 22, 0, 0, 0, 0, 0, 4, 4, 0, 0, 8, 0, 9, 0, 4, 4, 7, 9, 4, 4, 4, 0, 0, 0, 11, 8, 8, 16, 0, 0, 13, 13, 0, 0, 0, 27, 0, 12, 0, 4, 4, 0, 9, 8, 8, 0, 0, 0, 0, 6, 10, 0, 0, 0, 0, 0, 19, 5, 5, 0, 0, 4, 4, 4, 4, 4, 0], - # well then i must make some suggestions to you - # 1580/141084/1580_141084_000057_000000.wav - [0, 0, 0, 0, 0, 0, 0, 18, 0, 5, 0, 15, 0, 0, 15, 15, 4, 4, 0, 0, 6, 11, 5, 0, 0, 0, 9, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 10, 0, 4, 4, 0, 17, 0, 16, 0, 0, 12, 0, 6, 0, 4, 4, 0, 17, 17, 7, 0, 26, 5, 5, 4, 4, 0, 12, 12, 8, 8, 17, 17, 5, 0, 4, 4, 4, 12, 12, 16, 0, 21, 0, 0, 0, 0, 21, 21, 0, 5, 0, 0, 0, 12, 0, 0, 0, 6, 6, 0, 10, 0, 8, 8, 9, 0, 0, 0, 0, 0, 0, 12, 0, 0, 4, 4, 0, 0, 6, 0, 8, 0, 4, 4, 4, 0, 0, 22, 22, 0, 8, 16, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0], - # some others too big cotton county - # 1995/1826/1995_1826_000010_000002.wav - [0, 0, 0, 0, 12, 0, 8, 0, 17, 5, 4, 4, 0, 8, 0, 0, 6, 11, 5, 0, 13, 13, 0, 0, 12, 0, 4, 4, 0, 0, 6, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 10, 0, 0, 0, 0, 21, 0, 0, 4, 4, 4, 0, 0, 0, 19, 0, 8, 0, 6, 6, 0, 0, 0, 6, 8, 0, 9, 9, 0, 0, 4, 0, 0, 0, 0, 19, 8, 8, 16, 0, 9, 9, 0, 0, 6, 6, 0, 0, 22, 0, 0, 0, 0, 4, 4, 0, 0, 0], - ] - parser = argparse.ArgumentParser() parser.add_argument('-text', type=str, help='Text to speak.', default='my father worked at the airport. he was air traffic control. he always knew when the president was flying in but was not allowed to tell anyone.') parser.add_argument('-opt_code_gen', type=str, help='Path to options YAML file used to train the code_gen model', default='D:\\dlas\\options\\train_encoder_build_ctc_alignments.yml') diff --git a/codes/scripts/audio/gen/use_gpt_tts.py b/codes/scripts/audio/gen/use_gpt_tts.py index 831c8049..2bff42c9 100644 --- a/codes/scripts/audio/gen/use_gpt_tts.py +++ b/codes/scripts/audio/gen/use_gpt_tts.py @@ -135,14 +135,17 @@ if __name__ == '__main__': with torch.no_grad(): print("Performing GPT inference..") samples = [] + ctc_codes = [] + samples_per_batch = args.num_samples//args.num_batches for b in tqdm(range(args.num_batches)): - codes = gpt.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) + codes, attentions = gpt.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=samples_per_batch, length_penalty=1, + return_attentions=True) padding_needed = 250 - codes.shape[1] codes = F.pad(codes, (0, padding_needed), value=stop_mel_token) samples.append(codes) + ctc_codes.extend(gpt.convert_attentions_to_aligned_codes(text, attentions, codes, conds.shape[1])) samples = torch.cat(samples, dim=0) - del gpt print("Loading CLIP..") clip = load_model_from_config(args.opt_clip, model_name=args.clip_model_name, also_load_savepoint=False, load_path=args.clip_model_path).cuda().eval() @@ -157,10 +160,12 @@ if __name__ == '__main__': cond_clip_results = cond_clip(conds[:, -1], samples, torch.full((samples.shape[0],), fill_value=samples.shape[1]*1024, dtype=torch.long, device='cuda'), return_loss=False) clip_results = clip_results * (1-args.cond_clip_weight) + cond_clip_results * args.cond_clip_weight - best_results = samples[torch.topk(clip_results, k=args.num_outputs).indices] + best_indices = torch.topk(clip_results, k=args.num_outputs).indices + best_results = samples[best_indices] + best_codes = [ctc_codes[i] for i in best_indices] - # Delete the GPT TTS model to free up GPU memory - del samples, clip + # Delete the GPT TTS and associated models to free up GPU memory before diffusion. + del samples, clip, gpt print("Loading DVAE..") dvae = load_model_from_config(args.opt_diffuse, args.dvae_model_name).cuda() diff --git a/codes/trainer/ExtensibleTrainer.py b/codes/trainer/ExtensibleTrainer.py index 5886a711..ccaac148 100644 --- a/codes/trainer/ExtensibleTrainer.py +++ b/codes/trainer/ExtensibleTrainer.py @@ -341,26 +341,6 @@ class ExtensibleTrainer(BaseModel): [e.before_optimize(state) for e in self.experiments] step.do_step(it) - if step.nan_counter > 10: - if self.auto_recover is None: - print("Detected NaN grads more than 10 steps in a row. Saving model weights and aborting.") - self.save(it) - self.save_training_state({'iter': it}) - raise ArithmeticError - else: - print(f"!!!!!!!!Detected NaN grads more than 10 steps in a row. Restoring to a state {self.auto_recover} saves ago.") - for k, ps in self.save_history.keys(): - if len(ps) < self.auto_recover: - print("Belay that - not enough saves were recorded. Failing instead.") - raise ArithmeticError - if k == '__state__': - self.resume_training(torch.load(ps[-self.auto_recover])) - else: - if k in self.networks.keys(): # This isn't always the case, for example for EMAs. - self.load_network(ps[-self.auto_recover], self.networks[k], strict=True) - if self.do_emas: - self.load_network(self.save_history[f'{k}_ema'][-self.auto_recover], self.emas[k], strict=True) - # Call into custom step hooks as well as update EMA params. for name, net in self.networks.items(): if hasattr(net, "custom_optimizer_step"):