109 lines
4.6 KiB
Python
109 lines
4.6 KiB
Python
from itertools import groupby
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer
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from data.audio.unsupervised_audio_dataset import load_audio
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from models.tacotron2.text import symbols, sequence_to_text
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from trainer.networks import register_model
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from utils.util import opt_get
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class Wav2VecWrapper(nn.Module):
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"""
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Basic wrapper class that makes Wav2Vec2 usable by DLAS.
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"""
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def __init__(self, vocab_size=148, basis_model='facebook/wav2vec2-large', freeze_transformer=False, output_wer=True):
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super().__init__()
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self.w2v = Wav2Vec2ForCTC.from_pretrained(basis_model)
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# Perform some surgery to get the model we actually want.
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self.w2v.wav2vec2.encoder.gradient_checkpointing = True
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self.w2v.lm_head = nn.Linear(self.w2v.config.hidden_size, vocab_size)
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self.w2v.config.vocab_size = vocab_size
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self.w2v.config.pad_token_id = 0
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self.w2v.config.ctc_loss_reduction = 'mean'
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self.w2v.config.apply_spec_augment = True
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# We always freeze the feature extractor, which needs some special operations in DLAS
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for p in self.w2v.wav2vec2.feature_extractor.parameters():
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p.requires_grad = False
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p.DO_NOT_TRAIN = True
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if freeze_transformer:
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# Also freeze the encoder here.
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for p in list(self.w2v.wav2vec2.encoder.parameters()) + list(self.w2v.wav2vec2.feature_projection.parameters()):
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p.requires_grad = False
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p.DO_NOT_TRAIN = True
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self.output_wer = output_wer
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if output_wer:
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self.last_pred = []
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self.last_labels = []
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def forward(self, audio, unaligned_tokens, wav_lengths, text_lengths):
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audio = audio[:, :, :wav_lengths.max()]
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unaligned_tokens = unaligned_tokens[:, :text_lengths.max()]
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attention_mask = torch.ones_like(audio).squeeze(1)
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for b in range(audio.shape[0]):
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attention_mask[b, wav_lengths[b]:] = 0
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unaligned_tokens[b, text_lengths[b]:] = -100
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audio_norm = (audio - audio.mean()) / torch.sqrt(audio.var() + 1e-7)
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outputs = self.w2v(input_values=audio_norm.squeeze(1), attention_mask=attention_mask, labels=unaligned_tokens)
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if self.output_wer:
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self.last_pred.append(torch.argmax(outputs.logits, dim=-1))
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if len(self.last_pred) > 10:
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self.last_pred = self.last_pred[1:]
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self.last_labels.append(unaligned_tokens)
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if len(self.last_labels) > 10:
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self.last_labels = self.last_labels[1:]
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return outputs.loss
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def decode_ctc(self, output):
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if isinstance(output, torch.Tensor):
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output = output.tolist()
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tokens = [token_group[0] for token_group in groupby(output)]
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filtered_tokens = list(filter(lambda token: token != 0, tokens))
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return filtered_tokens
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def get_debug_values(self, step, net_name):
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res = {}
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if self.output_wer and step % 100 == 0:
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from datasets import load_metric
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wer_metric = load_metric("wer")
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label_strings = []
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pred_strings = []
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for last_labels, last_pred in zip(self.last_labels, self.last_pred):
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last_labels[last_labels == -100] = 0
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label_strings.extend([sequence_to_text(lbl) for lbl in last_labels])
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pred_strings.extend([sequence_to_text(self.decode_ctc(pred)) for pred in last_pred])
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wer = wer_metric.compute(predictions=pred_strings, references=label_strings)
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res['wer'] = wer
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print(f"Sample prediction: {pred_strings[0]} <=> {label_strings[0]}")
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return res
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def inference(self, audio):
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audio_norm = (audio - audio.mean()) / torch.sqrt(audio.var() + 1e-7)
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logits = self.w2v(input_values=audio_norm.squeeze(1)).logits
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pred = logits.argmax(dim=-1)
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return self.decode_ctc(pred)
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@register_model
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def register_wav2vec2_finetune(opt_net, opt):
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return Wav2VecWrapper(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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w2v = Wav2VecWrapper(basis_model='facebook/wav2vec2-large-960h', freeze_transformer=True)
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loss = w2v(torch.randn(2,1,50000), torch.randint(0,40,(2,70)), torch.tensor([20000, 30000]), torch.tensor([35, 50]))
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w2v.get_debug_values(0,"")
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sd = torch.load('../experiments/train_wav2vec_mass_archived_r0/models/19500_wav2vec.pth')
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w2v.load_state_dict(sd)
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pred = w2v.inference(load_audio('Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav', 16000).unsqueeze(0))
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res = sequence_to_text(pred[0])
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print(res)
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