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84 lines
3.2 KiB
YAML
84 lines
3.2 KiB
YAML
env:
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save_git_hash: True
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engine:
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model_path: null
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pretrained_name: null
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output_filename: null
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random_seed: &random_seed 42
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inference:
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mode: offline # choose from offline, chunked or offline_by_chunked
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chunk_len_in_secs: 1.6 #null # Need to specify if use buffered inference (default for offline_by_chunked is 20)
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total_buffer_in_secs: 4 #null # Need to specify if use buffered inference (default for offline_by_chunked is 22)
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model_stride: 8 # Model downsampling factor, 8 for Citrinet and FastConformer models, and 4 for Conformer models
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decoder_type: null # Used for hybrid CTC RNNT model only. Specify decoder_type *ctc* or *rnnt* for hybrid CTC RNNT model.
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test_ds:
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manifest_filepath: null
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sample_rate: 16000
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batch_size: 32
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num_workers: 4
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augmentor:
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silence:
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prob: 0.8
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min_start_silence_secs: 0
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max_start_silence_secs: 5
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min_end_silence_secs: 0
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max_end_silence_secs: 5
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rng: *random_seed
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noise:
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manifest_path: null
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prob: 0.8
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min_snr_db: 0
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max_snr_db: 15
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rng: *random_seed
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transcribe_params:
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# Put additional overrides for params in TranscriptionConfig used by transcribe_speech.py here
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# Don't put the following fields here: 'calculate_wer', 'model_path', 'pretrained_name', 'dataset_manifest',
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# 'output_filename', 'batch_size', 'num_workers', 'random_seed', 'eval_config_yaml', 'decoder_type'
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allow_partial_transcribe: False # only set True if your audio is too long and have 'offset' in manifest
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analyst:
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metric_calculator:
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exist_pred_manifest: null # specify the previously generated manifest will skip engine
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clean_groundtruth_text: True
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langid: "en" # speciify language to clean text. Note use text normalization in NeMo for better performancce
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output_filename: null
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use_cer: False
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ignore_capitalization: False
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ignore_punctuation: False
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punctuations: null # a string of punctuations to remove when ignore_punctuation=True. if not set, default to '!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~'
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strip_punc_space: False # strip spaces before punctuations. e.g., "I do ." -> "I do."
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metadata:
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duration:
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enable: True
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slot: [[0,2],[2,5],[5,10],[10,20],[20,100000]] # a slot accepts List[List[str]] or List[List[float]]. i.e. 1.8s belongs to slot [0,2]
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save_wer_per_class: False # whether to save wer for each presented class.
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gender:
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enable: False
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slot: [["female"]] # One could also report only one group/class though there are multiple classes in the data.
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save_wer_per_class: True
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speaker:
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enable: True
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save_wer_per_class: False
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age:
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enable: False
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slot: null
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save_wer_per_class: False
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emotion:
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enable: True
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slot: [['happy','laugh'],['neutral'],['sad']]
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save_wer_per_class: False
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writer:
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report_filename: null |