Delete Paraformer-large-Chuan

Paraformer-large-Chuan/am.mvn

@@ -1,8 +0,0 @@
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-[ 0 ]
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-</Nnet> 

Paraformer-large-Chuan/config.yaml

@@ -1,126 +0,0 @@
-model: Paraformer
-model_conf:
-  ctc_weight: 0.0
-  lsm_weight: 0.1
-  length_normalized_loss: true
-  predictor_weight: 1.0
-  predictor_bias: 1
-  sampling_ratio: 0.75
-encoder: SANMEncoder
-encoder_conf:
-  output_size: 512
-  attention_heads: 4
-  linear_units: 2048
-  num_blocks: 50
-  dropout_rate: 0.1
-  positional_dropout_rate: 0.1
-  attention_dropout_rate: 0.1
-  input_layer: pe
-  pos_enc_class: SinusoidalPositionEncoder
-  normalize_before: true
-  kernel_size: 11
-  sanm_shfit: 0
-  selfattention_layer_type: sanm
-decoder: ParaformerSANMDecoder
-decoder_conf:
-  attention_heads: 4
-  linear_units: 2048
-  num_blocks: 16
-  dropout_rate: 0.1
-  positional_dropout_rate: 0.1
-  self_attention_dropout_rate: 0.1
-  src_attention_dropout_rate: 0.1
-  att_layer_num: 16
-  kernel_size: 11
-  sanm_shfit: 0
-predictor: CifPredictorV2
-predictor_conf:
-  idim: 512
-  threshold: 1.0
-  l_order: 1
-  r_order: 1
-  tail_threshold: 0.45
-frontend: WavFrontend
-frontend_conf:
-  fs: 16000
-  window: hamming
-  n_mels: 80
-  frame_length: 25
-  frame_shift: 10
-  lfr_m: 7
-  lfr_n: 6
-  cmvn_file: ./speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/am.mvn
-specaug: SpecAugLFR
-specaug_conf:
-  apply_time_warp: false
-  time_warp_window: 5
-  time_warp_mode: bicubic
-  apply_freq_mask: true
-  freq_mask_width_range:
-  - 0
-  - 30
-  lfr_rate: 6
-  num_freq_mask: 1
-  apply_time_mask: true
-  time_mask_width_range:
-  - 0
-  - 12
-  num_time_mask: 1
-train_conf:
-  accum_grad: 1
-  grad_clip: 5
-  max_epoch: 5
-  val_scheduler_criterion:
-  - valid
-  - acc
-  best_model_criterion:
-  - - valid
-    - acc
-    - max
-  keep_nbest_models: 100
-  log_interval: 500
-  resume: true
-  validate_interval: 5000
-  save_checkpoint_interval: 5000
-  avg_nbest_model: 10
-  use_deepspeed: false
-  deepspeed_config: ./config/ds_stage1.json
-optim: adam
-optim_conf:
-  lr: 0.0002
-scheduler: warmuplr
-scheduler_conf:
-  warmup_steps: 30000
-dataset: AudioDataset
-dataset_conf:
-  index_ds: IndexDSJsonl
-  batch_sampler: BatchSampler
-  batch_type: token
-  batch_size: 300
-  max_token_length: 2048
-  buffer_size: 500
-  shuffle: true
-  num_workers: 4
-  data_split_num: 1
-  sort_size: 1024
-tokenizer: CharTokenizer
-tokenizer_conf:
-  unk_symbol: <unk>
-  split_with_space: true
-  token_list: ./speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.json
-  seg_dict_file: ./speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/seg_dict
-input_size: 560
-ctc_conf:
-  dropout_rate: 0.0
-  ctc_type: builtin
-  reduce: true
-  ignore_nan_grad: true
-normalize: null
-init_param: /home/work_nfs9/sywang/code/paraformer/outputs/model.pt
-config: /home/work_nfs9/sywang/code/paraformer/outputs/config.yaml
-is_training: true
-train_data_set_list: data/train.jsonl
-valid_data_set_list: data/val.jsonl
-output_dir: ./outputs
-model_path: /home/work_nfs9/sywang/code/paraformer/outputs
-device: cpu

Paraformer-large-Chuan/infer.py

@@ -1,65 +0,0 @@
-import argparse
-import json
-import os
-from funasr import AutoModel
-
-
-def read_wav_scp(wav_scp_file: str):
-    """读取 wav.scp 文件，返回 (id, wav_path) 元组列表。"""
-    wav_files = []
-    with open(wav_scp_file, 'r') as f:
-        for line in f:
-            id, wav_path = line.strip().split(" ", 1)  # 只根据第一个空格切分
-            wav_files.append((id, wav_path))
-    return wav_files
-
-
-def save_results(results, output_file: str):
-    """将推理结果保存到指定的文件中，格式为 'key text' 每行一条。"""
-    with open(output_file, 'w') as f:
-        for result in results:
-            key = result.get("key", "")
-            text = result.get("text", "")
-            f.write(f"{key} {text}\n")
-
-
-def main():
-    # 解析命令行参数
-    parser = argparse.ArgumentParser(description="Run speech recognition inference")
-    parser.add_argument('--model', type=str, required=True, help="Model name or path")
-    parser.add_argument('--wav_scp_file', type=str, required=True, help="Path to wav.scp file")
-    parser.add_argument('--output_dir', type=str, required=True, help="Directory to save inference results")
-    parser.add_argument('--device', type=str, default="cpu", choices=["cpu", "cuda"], help="Device to run inference on")
-    parser.add_argument('--output_file', type=str, required=True, help="File to save the inference results")
-
-    args = parser.parse_args()
-
-    # 初始化模型
-    print(f"Initializing model {args.model}...")
-    model = AutoModel(model=args.model, device=args.device)
-
-    # 读取 wav.scp 文件
-    wav_files = read_wav_scp(args.wav_scp_file)
-
-    # 存储所有推理结果
-    all_results = []
-
-    # 遍历每个音频文件并进行推理
-    for id, wav_path in wav_files:
-        print(f"正在处理音频文件 {id}: {wav_path}")
-        res = model.generate(wav_path)
-        print(f"推理结果: {res}")
-
-        if res:
-            # 提取推理结果中的 key 和 text
-            key = id
-            text = res[0].get("text", "")
-            all_results.append({"key": key, "text": text})
-
-    # 将推理结果保存到文件
-    save_results(all_results, args.output_file)
-    print(f"推理结果已保存到 {args.output_file}")
-
-
-if __name__ == "__main__":
-    main()

Paraformer-large-Chuan/model.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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-size 2642208221