kimythanly / voice-conversion-evaluation

An evaluation toolkit for voice conversion models.

Voice-conversion-evaluation

An evaluation toolkit for voice conversion models.

Sample test pair

Generate the metadata for evaluating models.
The directory of parsers contains several available corpus parsers.

  python sampler.py [name of source corpus] [path of source dir] [name of target corpus] [path of target dir] -n [number of samples] -nt [number of target utterances] -o [path of output dir]

The pairs of metadata are sorted by src_second for long to short.
The metadata contains:

• source_corpus: The name of the source corpus.
• source_corpus_speaker_number: The number of speaker in source corpus.
• source_random_seed: Random seed used for sampling source utterance.
• target_corpus: The name of the target corpus.
• target_corpus_speaker_number: The number of speaker in target corpus.
• target_random_seed: Random seed used for sampling target utterances.
• n_samples: number of samples
• n_target_samples: number of target utterances
• pairs: List of evaluating pairs
  • source_speaker: The name of the source speaker.
  • target_speaker: The name of the target speaker.
  • src_utt: The relative path of the source utterance, which is relative to the source dir.
  • tgt_utts: List of the relative path of target utterances, which is relative to the target dir.
  • content: The content of the source utterance.
  • src_second: The second of the source utterance.
  • converted: The entry does not appear when use sampler, you need to add the relative path for your converted output.

Metrics

The metrics include automatic mean opinion score assessment, character error rate, and speaker verification acceptance rate.

• Automatic mean opinion score assessment
  • Ensemble several MBNet which is implemented by sky1456723.

    python calculate_objective_metric.py -d [data_dir] -r metrics/mean_opinion_score
  

• Character error rate:
  • Use the automatic speech recognition model provided by Hugging Face.
  • The word error rate on Librispeech test-other is 3.9.

    python calculate_objective_metric.py -d [data_dir] -r metrics/character_error_rate
  

• Speaker verification acceptance rate:
  • You can calculate the threshold by metrics/speaker_verification/equal_error_rate/.
  • And some pre-calculated thresholds are in metrics/speaker_verification/equal_error_rate/threshold.yaml.

    python calculate_objective_metric.py -d [data_dir] -r metrics/speaker_verification -t [target_dir] -th [threshold path]