diff --git a/examples/aishell/conformer/run.sh b/examples/aishell/conformer/run.sh index 7bfca922b..ff99f9e2d 100755 --- a/examples/aishell/conformer/run.sh +++ b/examples/aishell/conformer/run.sh @@ -105,7 +105,8 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then echo "stage 4: ASR Training" mkdir -p ${exp_dir}/exp/${model_dir} - log_file="${exp_dir}/exp/${model_dir}/train.log.txt" + current_time=$(date "+%Y-%m-%d_%H-%M") + log_file="${exp_dir}/exp/${model_dir}/train.log.txt.${current_time}" echo "log_file: ${log_file}" gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}') diff --git a/funasr/datasets/audio_datasets/preprocessor.py b/funasr/datasets/audio_datasets/preprocessor.py index a3ba3a578..ab751401b 100644 --- a/funasr/datasets/audio_datasets/preprocessor.py +++ b/funasr/datasets/audio_datasets/preprocessor.py @@ -26,10 +26,11 @@ class SpeechPreprocessSpeedPerturb(nn.Module): return waveform speed = random.choice(self.speed_perturb) if speed != 1.0: - with torch.no_grad(): - waveform, _ = torchaudio.sox_effects.apply_effects_tensor( - torch.tensor(waveform).view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]]) - waveform = waveform.view(-1) + if not isinstance(waveform, torch.Tensor): + waveform = torch.tensor(waveform) + waveform, _ = torchaudio.sox_effects.apply_effects_tensor( + waveform.view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]]) + waveform = waveform.view(-1) return waveform diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py index f99161a7e..10f7f80f0 100644 --- a/funasr/train_utils/trainer.py +++ b/funasr/train_utils/trainer.py @@ -70,6 +70,7 @@ class Trainer: self.avg_nbest_model = kwargs.get("avg_nbest_model", 5) self.kwargs = kwargs self.log_interval = kwargs.get("log_interval", 50) + self.batch_total = 0 try: @@ -196,7 +197,9 @@ class Trainer: self.optim.zero_grad() speed_stats = {} time5 = time.perf_counter() + for batch_idx, batch in enumerate(self.dataloader_train): + self.batch_total += 1 time1 = time.perf_counter() speed_stats["data_load"] = f"{time1-time5:0.3f}" @@ -205,25 +208,10 @@ class Trainer: my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext with my_context(): time2 = time.perf_counter() - # print("before, GPU, memory: {:.3f} GB, " - # "{:.3f} GB, " - # "{:.3f} GB, " - # "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024, - # torch.cuda.max_memory_allocated()/1024/1024/1024, - # torch.cuda.memory_reserved()/1024/1024/1024, - # torch.cuda.max_memory_reserved()/1024/1024/1024, - # )) retval = self.model(**batch) torch.cuda.empty_cache() - # print("after, GPU, memory: {:.3f} GB, " - # "{:.3f} GB, " - # "{:.3f} GB, " - # "{:.3f} GB".format(torch.cuda.memory_allocated()/1024/1024/1024, - # torch.cuda.max_memory_allocated()/1024/1024/1024, - # torch.cuda.memory_reserved()/1024/1024/1024, - # torch.cuda.max_memory_reserved()/1024/1024/1024, - # )) + time3 = time.perf_counter() speed_stats["forward_time"] = f"{time3 - time2:0.3f}" loss, stats, weight = retval @@ -275,7 +263,7 @@ class Trainer: - if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1: + if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_train): pbar.update(self.log_interval) gpu_info = "GPU, memory: {:.3f} GB, " \ "{:.3f} GB, "\ @@ -287,22 +275,22 @@ class Trainer: ) description = ( f"rank: {self.local_rank}, " - f"Train epoch: {epoch}/{self.max_epoch}, " - f"step {batch_idx}/{len(self.dataloader_train)}, " - f"{speed_stats}, " + f"epoch: {epoch}/{self.max_epoch}, " + f"step: {batch_idx}/{len(self.dataloader_train)}, total: {self.batch_total}, " f"(loss: {loss.detach().cpu().item():.3f}), " f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}" + f"{speed_stats}, " f"{gpu_info}" ) pbar.set_description(description) if self.writer: - self.writer.add_scalar(f'rank{self.local_rank}, Loss/train', loss.item(), + self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(), epoch*len(self.dataloader_train) + batch_idx) for key, var in stats.items(): - self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', var.item(), + self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(), epoch * len(self.dataloader_train) + batch_idx) for key, var in speed_stats.items(): - self.writer.add_scalar(f'rank{self.local_rank}, {key}/train', eval(var), + self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var), epoch * len(self.dataloader_train) + batch_idx) # if batch_idx == 2: @@ -348,24 +336,23 @@ class Trainer: time4 = time.perf_counter() - if batch_idx % self.log_interval == 0 or batch_idx == len(self.dataloader_train) - 1: + if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_val): pbar.update(self.log_interval) description = ( f"rank: {self.local_rank}, " f"validation epoch: {epoch}/{self.max_epoch}, " - f"step {batch_idx}/{len(self.dataloader_train)}, " - f"{speed_stats}, " + f"step: {batch_idx}/{len(self.dataloader_val)}, " f"(loss: {loss.detach().cpu().item():.3f}), " f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}" - f"rank: {self.local_rank}" + f"{speed_stats}, " ) pbar.set_description(description) if self.writer: - self.writer.add_scalar(f"rank{self.local_rank}, Loss/val", loss.item(), - epoch*len(self.dataloader_train) + batch_idx) + self.writer.add_scalar(f"rank{self.local_rank}_Loss/val", loss.item(), + epoch*len(self.dataloader_val) + batch_idx) for key, var in stats.items(): - self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', var.item(), - epoch * len(self.dataloader_train) + batch_idx) + self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', var.item(), + epoch * len(self.dataloader_val) + batch_idx) for key, var in speed_stats.items(): - self.writer.add_scalar(f'rank{self.local_rank}, {key}/val', eval(var), - epoch * len(self.dataloader_train) + batch_idx) \ No newline at end of file + self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', eval(var), + epoch * len(self.dataloader_val) + batch_idx) \ No newline at end of file