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Paraformer-large-Chuan/am.mvn

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+[ 0 ]
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+</Nnet> 

Paraformer-large-Chuan/config.yaml

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+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/model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3cb8f6c514ada6029c3504f6629a4756a583fe801e56a383a095636dc4c3ee77
+size 2642208221

infer_paraformer.py

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+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()