egs/metrics/README.md

Amphion Evaluation Recipe

Supported Evaluation Metrics

Until now, Amphion Evaluation has supported the following objective metrics:

• F0 Modeling:
  • F0 Pearson Coefficients (FPC)
  • F0 Periodicity Root Mean Square Error (PeriodicityRMSE)
  • F0 Root Mean Square Error (F0RMSE)
  • Voiced/Unvoiced F1 Score (V/UV F1)
• Energy Modeling:
  • Energy Root Mean Square Error (EnergyRMSE)
  • Energy Pearson Coefficients (EnergyPC)
• Intelligibility:
  • Character Error Rate (CER) based on Whipser
  • Word Error Rate (WER) based on Whipser
• Spectrogram Distortion:
  • Frechet Audio Distance (FAD)
  • Mel Cepstral Distortion (MCD)
  • Multi-Resolution STFT Distance (MSTFT)
  • Perceptual Evaluation of Speech Quality (PESQ)
  • Short Time Objective Intelligibility (STOI)
  • Scale Invariant Signal to Distortion Ratio (SISDR)
  • Scale Invariant Signal to Noise Ratio (SISNR)
• Speaker Similarity:
  • Cosine similarity based on Rawnet3
  • Cosine similarity based on WeSpeaker (👨‍💻 developing)

We provide a recipe to demonstrate how to objectively evaluate your generated audios. There are three steps in total:

1. Pretrained Models Preparation
2. Audio Data Preparation
3. Evaluation

1. Pretrained Models Preparation

If you want to calculate RawNet3 based speaker similarity, you need to download the pretrained model first, as illustrated here.

2. Aduio Data Preparation

Prepare reference audios and generated audios in two folders, the ref_dir contains the reference audio and the gen_dir contains the generated audio. Here is an example.

 ┣ {ref_dir}
 ┃ ┣ sample1.wav
 ┃ ┣ sample2.wav
 ┣ {gen_dir}
 ┃ ┣ sample1.wav
 ┃ ┣ sample2.wav

You have to make sure that the pairwise reference audio and generated audio are named the same, as illustrated above (sample1 to sample1, sample2 to sample2).

3. Evaluation

Run the run.sh with specified refenrece folder, generated folder, dump folder and metrics.

cd Amphion
sh egs/metrics/run.sh \
    --reference_folder [Your path to the reference audios] \
    --generated_folder [Your path to the generated audios] \
    --dump_folder [Your path to dump the objective results] \
    --metrics [The metrics you need] \

As for the metrics, an example is provided below:

--metrics "mcd pesq fad"

All currently available metrics keywords are listed below:

Keys	Description
fpc	F0 Pearson Coefficients
f0_periodicity_rmse	F0 Periodicity Root Mean Square Error
f0rmse	F0 Root Mean Square Error
v_uv_f1	Voiced/Unvoiced F1 Score
energy_rmse	Energy Root Mean Square Error
energy_pc	Energy Pearson Coefficients
cer	Character Error Rate
wer	Word Error Rate
speaker_similarity	Cos Similarity based on RawNet3
fad	Frechet Audio Distance
mcd	Mel Cepstral Distortion
mstft	Multi-Resolution STFT Distance
pesq	Perceptual Evaluation of Speech Quality
si_sdr	Scale Invariant Signal to Distortion Ratio
si_snr	Scale Invariant Signal to Noise Ratio
stoi	Short Time Objective Intelligibility