espnet2.asr_transducer.error_calculator.ErrorCalculator

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class espnet2.asr_transducer.error_calculator.ErrorCalculator(decoder: AbsDecoder, joint_network: JointNetwork, token_list: List[int], sym_space: str, sym_blank: str, nstep: int = 2, report_cer: bool = False, report_wer: bool = False)

Bases: object

Calculate CER and WER for transducer models.

• Parameters:
  • decoder – Decoder module.
  • joint_network – Joint Network module.
  • token_list – List of token units.
  • sym_space – Space symbol.
  • sym_blank – Blank symbol.
  • nstep – Maximum number of symbol expansions at each time step w/ mAES.
  • report_cer – Whether to compute CER.
  • report_wer – Whether to compute WER.

Construct an ErrorCalculatorTransducer object.

calculate_cer(char_pred: Tensor, char_target: Tensor) → float

Calculate sentence-level CER score.

• Parameters:
  • char_pred – Prediction character sequences. (B, ?)
  • char_target – Target character sequences. (B, ?)
• Returns: Average sentence-level CER score.

calculate_wer(char_pred: Tensor, char_target: Tensor) → float

Calculate sentence-level WER score.

• Parameters:
  • char_pred – Prediction character sequences. (B, ?)
  • char_target – Target character sequences. (B, ?)
• Returns: Average sentence-level WER score

convert_to_char(pred: Tensor, target: Tensor) → Tuple[List, List]

Convert label ID sequences to character sequences.

• Parameters:
  • pred – Prediction label ID sequences. (B, U)
  • target – Target label ID sequences. (B, L)
• Returns: Prediction character sequences. (B, ?) char_target: Target character sequences. (B, ?)
• Return type: char_pred