#!/usr/bin/env/python3 import logging import sys from pathlib import Path import os import librosa import torch from torch.utils.data import DataLoader from hyperpyyaml import load_hyperpyyaml import speechbrain as sb from speechbrain.utils.distributed import if_main_process, run_on_main from jiwer import wer, cer logger = logging.getLogger(__name__) # Define training procedure class ASR(sb.Brain): def compute_forward(self, batch, stage): """Forward computations from the waveform batches to the output probabilities.""" batch = batch.to(self.device) sig, self.sig_lens = batch.sig tokens_bos, _ = batch.tokens_bos sig, self.sig_lens = sig.to(self.device), self.sig_lens.to(self.device) # Add waveform augmentation if specified. if stage == sb.Stage.TRAIN: sig, self.sig_lens = self.hparams.wav_augment(sig, self.sig_lens) # Forward pass encoded_outputs = self.modules.encoder_w2v2(sig.detach()) embedded_tokens = self.modules.embedding(tokens_bos) decoder_outputs, _ = self.modules.decoder(embedded_tokens, encoded_outputs, self.sig_lens) # Output layer for seq2seq log-probabilities logits = self.modules.seq_lin(decoder_outputs) predictions = {"seq_logprobs": self.hparams.log_softmax(logits)} if self.is_ctc_active(stage): # Output layer for ctc log-probabilities ctc_logits = self.modules.ctc_lin(encoded_outputs) predictions["ctc_logprobs"] = self.hparams.log_softmax(ctc_logits) elif stage == sb.Stage.VALID: predictions["tokens"], _, _, _ = self.hparams.greedy_search(encoded_outputs, self.sig_lens) elif stage == sb.Stage.TEST: predictions["tokens"], _, _, _ = self.hparams.test_search(encoded_outputs, self.sig_lens) return predictions def is_ctc_active(self, stage): """Check if CTC is currently active. Arguments --------- stage : sb.Stage Currently executing stage. """ if stage != sb.Stage.TRAIN: return False current_epoch = self.hparams.epoch_counter.current return current_epoch <= self.hparams.number_of_ctc_epochs def compute_objectives(self, predictions, batch, stage): """Computes the loss (CTC+NLL) given predictions and targets.""" ids = batch.id tokens_eos, tokens_eos_lens = batch.tokens_eos tokens, tokens_lens = batch.tokens loss = self.hparams.nll_cost(log_probabilities=predictions["seq_logprobs"], targets=tokens_eos, length=tokens_eos_lens) if self.is_ctc_active(stage): # Load tokens without EOS as CTC targets loss_ctc = self.hparams.ctc_cost(predictions["ctc_logprobs"], tokens, self.sig_lens, tokens_lens) loss *= 1 - self.hparams.ctc_weight loss += self.hparams.ctc_weight * loss_ctc if stage != sb.Stage.TRAIN: predicted_words = [self.hparams.tokenizer.decode_ids(prediction).split(" ") for prediction in predictions["tokens"]] target_words = [words.split(" ") for words in batch.transcript] self.wer_metric.append(ids, predicted_words, target_words) self.cer_metric.append(ids, predicted_words, target_words) return loss def on_stage_start(self, stage, epoch): """Gets called at the beginning of each epoch""" if stage != sb.Stage.TRAIN: self.cer_metric = self.hparams.cer_computer() self.wer_metric = self.hparams.error_rate_computer() def on_stage_end(self, stage, stage_loss, epoch): """Gets called at the end of a epoch.""" # Compute/store important stats stage_stats = {"loss": stage_loss} if stage == sb.Stage.TRAIN: self.train_stats = stage_stats else: stage_stats["CER"] = self.cer_metric.summarize("error_rate") stage_stats["WER"] = self.wer_metric.summarize("error_rate") # Perform end-of-iteration things, like annealing, logging, etc. if stage == sb.Stage.VALID: old_lr, new_lr = self.hparams.lr_annealing(stage_stats["WER"]) sb.nnet.schedulers.update_learning_rate(self.optimizer, new_lr) self.hparams.train_logger.log_stats( stats_meta={"epoch": epoch, "lr": old_lr}, train_stats=self.train_stats, valid_stats=stage_stats, ) self.checkpointer.save_and_keep_only( meta={"WER": stage_stats["WER"]}, min_keys=["WER"], ) elif stage == sb.Stage.TEST: self.hparams.train_logger.log_stats( stats_meta={"Epoch loaded": self.hparams.epoch_counter.current}, test_stats=stage_stats, ) if if_main_process(): with open(self.hparams.test_wer_file, "w") as w: self.wer_metric.write_stats(w) def run_inference( self, dataset, # Must be obtained from the dataio_function min_key, # We load the model with the lowest error rate loader_kwargs, # opts for the dataloading ): # If dataset isn't a Dataloader, we create it. if not isinstance(dataset, DataLoader): loader_kwargs["ckpt_prefix"] = None dataset = self.make_dataloader( dataset, sb.Stage.TEST, **loader_kwargs ) self.checkpointer.recover_if_possible(min_key=min_key) self.modules.eval() # We set the model to eval mode (remove dropout etc) with torch.no_grad(): true_labels = [] pred_labels = [] for batch in dataset: # Make sure that your compute_forward returns the predictions !!! # In the case of the template, when stage = TEST, a beam search is applied # in compute_forward(). predictions = self.compute_forward(batch, stage=sb.Stage.TEST) pred_batch = [] predicted_words = [] predicted_words = [self.hparams.tokenizer.decode_ids(prediction).split(" ") for prediction in predictions["tokens"]] for sent in predicted_words: # sent = " ".join(sent) sent = filter_repetitions(sent, 3) sent = " ".join(sent) pred_batch.append(sent) pred_labels.append(pred_batch[0]) true_labels.append(batch.transcript[0]) print('WER: ', wer(true_labels, pred_labels) * 100) print('CER: ', cer(true_labels, pred_labels) * 100) def filter_repetitions(seq, max_repetition_length): seq = list(seq) output = [] max_n = len(seq) // 2 for n in range(max_n, 0, -1): max_repetitions = max(max_repetition_length // n, 1) # Don't need to iterate over impossible n values: # len(seq) can change a lot during iteration if (len(seq) <= n*2) or (len(seq) <= max_repetition_length): continue iterator = enumerate(seq) # Fill first buffers: buffers = [[next(iterator)[1]] for _ in range(n)] for seq_index, token in iterator: current_buffer = seq_index % n if token != buffers[current_buffer][-1]: # No repeat, we can flush some tokens buf_len = sum(map(len, buffers)) flush_start = (current_buffer-buf_len) % n # Keep n-1 tokens, but possibly mark some for removal for flush_index in range(buf_len - buf_len%n): if (buf_len - flush_index) > n-1: to_flush = buffers[(flush_index + flush_start) % n].pop(0) else: to_flush = None # Here, repetitions get removed: if (flush_index // n < max_repetitions) and to_flush is not None: output.append(to_flush) elif (flush_index // n >= max_repetitions) and to_flush is None: output.append(to_flush) buffers[current_buffer].append(token) # At the end, final flush current_buffer += 1 buf_len = sum(map(len, buffers)) flush_start = (current_buffer-buf_len) % n for flush_index in range(buf_len): to_flush = buffers[(flush_index + flush_start) % n].pop(0) # Here, repetitions just get removed: if flush_index // n < max_repetitions: output.append(to_flush) seq = [] to_delete = 0 for token in output: if token is None: to_delete += 1 elif to_delete > 0: to_delete -= 1 else: seq.append(token) output = [] return seq def dataio_prepare(hparams): """This function prepares the datasets to be used in the brain class. It also defines the data processing pipeline through user-defined functions. """ data_folder = hparams["data_folder"] train_data = sb.dataio.dataset.DynamicItemDataset.from_json(json_path=os.path.join(hparams["data_folder"], "train.json"), replacements={"data_root": data_folder}) train_data = train_data.filtered_sorted(sort_key="duration") hparams["train_dataloader_opts"]["shuffle"] = False valid_data = sb.dataio.dataset.DynamicItemDataset.from_json(json_path=os.path.join(hparams["data_folder"], "dev.json"), replacements={"data_root": data_folder}) valid_data = valid_data.filtered_sorted(sort_key="duration") test_data = sb.dataio.dataset.DynamicItemDataset.from_json(json_path=os.path.join(hparams["data_folder"], "test.json"), replacements={"data_root": data_folder}) datasets = [train_data, valid_data, test_data] # We get the tokenizer as we need it to encode the labels when creating # mini-batches. tokenizer = hparams["tokenizer"] # 2. Define audio pipeline: @sb.utils.data_pipeline.takes("data_path") @sb.utils.data_pipeline.provides("sig") def audio_pipeline(data_path): sig, sr = librosa.load(data_path, sr=16000) # sig = sb.dataio.dataio.read_audio(wav) # alternatively use the SpeechBrain data loading function return sig sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline) # 3. Define text pipeline: @sb.utils.data_pipeline.takes("transcript") @sb.utils.data_pipeline.provides("transcript", "tokens_list", "tokens_bos", "tokens_eos", "tokens") def text_pipeline(transcript): yield transcript tokens_list = tokenizer.encode_as_ids(transcript) yield tokens_list tokens_bos = torch.LongTensor([hparams["bos_index"]] + (tokens_list)) yield tokens_bos tokens_eos = torch.LongTensor(tokens_list + [hparams["eos_index"]]) yield tokens_eos tokens = torch.LongTensor(tokens_list) yield tokens sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline) # 4. Set output: sb.dataio.dataset.set_output_keys(datasets, ["id", "sig", "transcript", "tokens_list", "tokens_bos", "tokens_eos", "tokens"]) return (train_data, valid_data, test_data) if __name__ == "__main__": # CLI: hparams_file, run_opts, overrides = sb.parse_arguments(sys.argv[1:]) # create ddp_group with the right communication protocol sb.utils.distributed.ddp_init_group(run_opts) with open(hparams_file) as fin: hparams = load_hyperpyyaml(fin, overrides) # Create experiment directory sb.create_experiment_directory( experiment_directory=hparams["output_folder"], hyperparams_to_save=hparams_file, overrides=overrides, ) # here we create the datasets objects as well as tokenization and encoding (train_data, valid_data, test_data) = dataio_prepare(hparams) run_on_main(hparams["pretrainer"].collect_files) hparams["pretrainer"].load_collected() # Trainer initialization asr_brain = ASR( modules=hparams["modules"], opt_class=hparams["opt_class"], hparams=hparams, run_opts=run_opts, checkpointer=hparams["checkpointer"], ) # We dynamically add the tokenizer to our brain class. # NB: This tokenizer corresponds to the one used for the LM!! asr_brain.tokenizer = hparams["tokenizer"] train_dataloader_opts = hparams["train_dataloader_opts"] valid_dataloader_opts = hparams["valid_dataloader_opts"] # Training/validation loop if hparams["skip_training"] == False: print("Training...") # Training asr_brain.fit( asr_brain.hparams.epoch_counter, train_data, valid_data, train_loader_kwargs=train_dataloader_opts, valid_loader_kwargs=valid_dataloader_opts, ) else: # evaluate print("Evaluating") asr_brain.run_inference(test_data, "WER", hparams["test_dataloader_opts"])