仿生人AI服务端
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encoder: SenseVoiceEncoderSmall
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encoder_conf:
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output_size: 512
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attention_heads: 4
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linear_units: 2048
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num_blocks: 50
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tp_blocks: 20
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dropout_rate: 0.1
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positional_dropout_rate: 0.1
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attention_dropout_rate: 0.1
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input_layer: pe
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pos_enc_class: SinusoidalPositionEncoder
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normalize_before: true
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kernel_size: 11
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sanm_shfit: 0
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selfattention_layer_type: sanm
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model: SenseVoiceSmall
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model_conf:
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length_normalized_loss: true
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sos: 1
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eos: 2
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ignore_id: -1
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tokenizer: SentencepiecesTokenizer
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tokenizer_conf:
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bpemodel: null
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unk_symbol: <unk>
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split_with_space: true
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frontend: WavFrontend
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frontend_conf:
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fs: 16000
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window: hamming
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n_mels: 80
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frame_length: 25
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frame_shift: 10
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lfr_m: 7
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lfr_n: 6
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cmvn_file: null
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dataset: SenseVoiceCTCDataset
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dataset_conf:
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index_ds: IndexDSJsonl
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batch_sampler: EspnetStyleBatchSampler
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data_split_num: 32
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batch_type: token
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batch_size: 14000
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max_token_length: 2000
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min_token_length: 60
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max_source_length: 2000
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min_source_length: 60
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max_target_length: 200
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min_target_length: 0
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shuffle: true
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num_workers: 4
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sos: ${model_conf.sos}
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eos: ${model_conf.eos}
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IndexDSJsonl: IndexDSJsonl
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retry: 20
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train_conf:
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accum_grad: 1
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grad_clip: 5
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max_epoch: 20
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keep_nbest_models: 10
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avg_nbest_model: 10
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log_interval: 100
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resume: true
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validate_interval: 10000
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save_checkpoint_interval: 10000
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optim: adamw
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optim_conf:
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lr: 0.00002
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scheduler: warmuplr
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scheduler_conf:
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warmup_steps: 25000
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specaug: SpecAugLFR
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specaug_conf:
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apply_time_warp: false
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time_warp_window: 5
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time_warp_mode: bicubic
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apply_freq_mask: true
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freq_mask_width_range:
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- 0
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- 30
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lfr_rate: 6
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num_freq_mask: 1
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apply_time_mask: true
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time_mask_width_range:
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- 0
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- 12
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num_time_mask: 1
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{
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"framework": "pytorch",
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"task" : "auto-speech-recognition",
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"model": {"type" : "funasr"},
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"pipeline": {"type":"funasr-pipeline"},
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"model_name_in_hub": {
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"ms":"",
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"hf":""},
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"file_path_metas": {
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"init_param":"model.pt",
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"config":"config.yaml",
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"tokenizer_conf": {"bpemodel": "chn_jpn_yue_eng_ko_spectok.bpe.model"},
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"frontend_conf":{"cmvn_file": "am.mvn"}}
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}
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from funasr import AutoModel
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from funasr.utils.postprocess_utils import rich_transcription_postprocess
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model_dir = "./"
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model = AutoModel(
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model=model_dir,
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vad_model="fsmn-vad",
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vad_kwargs={"max_single_segment_time": 30000},
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# device="cuda:0",
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hub="hf",
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)
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# en
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res = model.generate(
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input=f"{model.model_path}/example/en.mp3",
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cache={},
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language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=True,
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batch_size_s=60,
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merge_vad=True, #
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merge_length_s=15,
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)
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text = rich_transcription_postprocess(res[0]["text"])
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print(text)
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