Dev gzf (#1377)

zhifu gao

2024-02-21 cdca62d933c4e0766a05044c6cba7cfa0596e615

Dev gzf (#1377)

* update train recipe

* v1.0.8

* llm

* update trainer

 examples/industrial_data_pretraining/paraformer/demo.sh

 examples/industrial_data_pretraining/paraformer/finetune.sh

@@ -6,7 +6,7 @@
#git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}

## generate jsonl from wav.scp and text.txt
#python funasr/datasets/audio_datasets/scp2jsonl.py \
#python -m funasr.datasets.audio_datasets.scp2jsonl \
#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
#++data_type_list='["source", "target"]' \
#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl

 funasr/datasets/audio_datasets/scp2jsonl.py

@@ -72,14 +72,7 @@

@hydra.main(config_name=None, version_base=None)
def main_hydra(cfg: DictConfig):
    """
    python funasr/datasets/audio_datasets/scp2jsonl.py \
    ++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
    ++data_type_list='["source", "target"]' \
    ++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl

    """
    
 
    kwargs = OmegaConf.to_container(cfg, resolve=True)

    scp_file_list = kwargs.get("scp_file_list", ("/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"))
@@ -90,6 +83,13 @@
    gen_jsonl_from_wav_text_list(scp_file_list, data_type_list=data_type_list, jsonl_file_out=jsonl_file_out)
    

"""
python -m funasr.datasets.audio_datasets.scp2jsonl \
++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
++data_type_list='["source", "target"]' \
++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
"""

if __name__ == "__main__":
    main_hydra()


 funasr/models/mossformer/mossformer_encoder.py

@@ -4,7 +4,7 @@
try:
    from rotary_embedding_torch import RotaryEmbedding
except:
    print("Please install rotary_embedding_torch by: \n pip install -U rotary_embedding_torch")
    print("If you want use mossformer, lease install rotary_embedding_torch by: \n pip install -U rotary_embedding_torch")
from funasr.models.transformer.layer_norm import GlobalLayerNorm, CumulativeLayerNorm, ScaleNorm
from funasr.models.transformer.embedding import ScaledSinuEmbedding
from funasr.models.transformer.mossformer import FLASH_ShareA_FFConvM

 funasr/models/paraformer/model.py

@@ -455,7 +455,9 @@
            speech, speech_lengths = data_in, data_lengths
            if len(speech.shape) < 3:
                speech = speech[None, :, :]
            if speech_lengths is None:
            if speech_lengths is not None:
                speech_lengths = speech_lengths.squeeze(-1)
            else:
                speech_lengths = speech.shape[1]
        else:
            # extract fbank feats

 funasr/train_utils/trainer.py

@@ -181,7 +181,7 @@

            time2 = time.perf_counter()
            time_escaped = (time2 - time1)/3600.0
            print(f"\ntime_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f}\n")
            print(f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f}\n")

        if self.rank == 0:
            average_checkpoints(self.output_dir, self.avg_nbest_model)
@@ -302,17 +302,14 @@
                )
                pbar.set_description(description)
                if self.writer:
                    self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(),
                                           epoch*len(self.dataloader_train) + batch_idx)
                    self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(), self.batch_total)
                    self.writer.add_scalar(f'rank{self.local_rank}_lr/train', lr, self.batch_total)
                    for key, var in stats.items():
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(),
                                               epoch * len(self.dataloader_train) + batch_idx)
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(), self.batch_total)
                    for key, var in speed_stats.items():
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var),
                                               epoch * len(self.dataloader_train) + batch_idx)
                    
            # if batch_idx == 2:
            #     break
                        self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var), self.batch_total)


        pbar.close()

    def _validate_epoch(self, epoch):
@@ -356,7 +353,10 @@
                
                if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_val):
                    pbar.update(self.log_interval)
                    time_now = datetime.now()
                    time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
                    description = (
                        f"{time_now}, "
                        f"rank: {self.local_rank}, "
                        f"validation epoch: {epoch}/{self.max_epoch}, "
                        f"step: {batch_idx+1}/{len(self.dataloader_val)}, "

 funasr/version.txt

@@ -1 +1 @@
1.0.7
1.0.8