eval

Evaluation

Install packages for evaluation:

pip install -e .[eval]

Generating Samples for Evaluation

Prepare Test Datasets

1. Seed-TTS testset: Download from seed-tts-eval.
2. LibriSpeech test-clean: Download from OpenSLR.
3. Unzip the downloaded datasets and place them in the data/ directory.
4. Our filtered LibriSpeech-PC 4-10s subset: data/librispeech_pc_test_clean_cross_sentence.lst

Batch Inference for Test Set

To run batch inference for evaluations, execute the following commands:

# if not setup accelerate config yet
accelerate config

# if only perform inference
bash src/f5_tts/eval/eval_infer_batch.sh --infer-only

# if inference and with corresponding evaluation, setup the following tools first
bash src/f5_tts/eval/eval_infer_batch.sh

Objective Evaluation on Generated Results

Download Evaluation Model Checkpoints

1. Chinese ASR Model: Paraformer-zh
2. English ASR Model: Faster-Whisper
3. WavLM Model: Download from Google Drive.

Note

ASR model will be automatically downloaded if --local not set for evaluation scripts.
Otherwise, you should update the asr_ckpt_dir path values in eval_librispeech_test_clean.py or eval_seedtts_testset.py.

WavLM model must be downloaded and your wavlm_ckpt_dir path updated in eval_librispeech_test_clean.py and eval_seedtts_testset.py.

Objective Evaluation Examples

Update the path with your batch-inferenced results, and carry out WER / SIM / UTMOS evaluations:

# Evaluation [WER] for Seed-TTS test [ZH] set
python src/f5_tts/eval/eval_seedtts_testset.py --eval_task wer --lang zh --gen_wav_dir <GEN_WAV_DIR> --gpu_nums 8

# Evaluation [SIM] for LibriSpeech-PC test-clean (cross-sentence)
python src/f5_tts/eval/eval_librispeech_test_clean.py --eval_task sim --gen_wav_dir <GEN_WAV_DIR> --librispeech_test_clean_path <TEST_CLEAN_PATH>

# Evaluation [UTMOS]. --ext: Audio extension
python src/f5_tts/eval/eval_utmos.py --audio_dir <WAV_DIR> --ext wav

Note

Evaluation results can also be found in _*_results.jsonl files saved in <GEN_WAV_DIR>/<WAV_DIR>.