Datasets:

inclusionAI
/

Ming-Freeform-Audio-Edit-Benchmark

Semantic Editing (3 tasks):
- Free-form Deletion
- Free-form Insertion
- Free-form Substitution
Acoustic Editing (5 tasks):
- Time-stretching
- Pitch Shifting
- Dialect Conversion
- Emotion Conversion
- Volume Conversion

The audio samples are sourced from well-known open-source datasets, including seed-tts eval, LibriTTS, and Gigaspeech.

Dataset statistics

Semantic Editing

full version

Task Types\ # samples \ Language	Zh deletion	Zh insertion	Zh substitution	En deletion	En insertion	En substitution
Index-based	186	180	36	138	100	67
Content-based	95	110	289	62	99	189
Total	281	290	325	200	199	256

basic version

Task Types\ # samples \ Language	Zh deletion	Zh insertion	Zh substitution	En deletion	En insertion	En substitution
Index-based	92	65	29	47	79	29
Content-based	78	105	130	133	81	150
Total	170	170	159	180	160	179

Index-based instruction: specifies an operation on content at positions i to j. (e.g. delete the characters or words from index 3 to 12)

Content-based: targets specific characters or words for editing. (e.g. insert 'hello' before 'world')

Acoustic Editing

Task Types\ # samples \ Language	Zh	En
Time-stretching	50	50
Pitch Shifting	50	50
Dialect Conversion	250	---
Emotion Conversion	84	72
Volume Conversion	50	50

Evaluation Metrics

Environment Preparation

git clone https://github.com/inclusionAI/Ming-Freeform-Audio-Edit.git
cd Ming-Freeform-Audio-Edit
pip install -r requirements.txt

Note: Please download the audio and meta files from HuggingFace or ModelScope and put the wavs and meta directories under Ming-Freeform-Audio-Edit

Semantic Editing

For the deletion, insertion, and substitution tasks, we evaluate the performance using four key metrics:

Word Error Rate (WER) of the Edited Region (wer)
Word Error Rate (WER) of the Non-edited Region (wer.noedit)
Edit Operation Accuracy (acc)
Speaker Similarity (sim)

If you have organized the directories contain edited waveforms like below:
```
eval_path
 |
 ├── del
 │   └── edit_del_basic
 │       └── tts/ # This is the actual directory contains the edited wavs
 ├── ins
 │   └── edit_ins_basic
 │       └── tts/ # This is the actual directory contains the edited wavs
 ├── sub
   └── edit_sub_basic
       └── tts/ # This is the actual directory contains the edited wavs
```
Then you can run the following command to get those metrics:
```
cd Ming-Freeform-Audio-Edit/eval_scripts
bash run_eval_semantic.sh eval_path \
                          whisper_path \
                          paraformer_path \
                          wavlm_path \
                          eval_mode \
                          lang
```
Here is a brief description of the parameters for the script above:
- eval_path: The top-level directory containing subdirectories for each editing task
- whisper_path:Path to the Whisper model, which is used to calculate WER for English audio. You can download it from here.
- paraformer_path:Path to the Paraformer model, which is used to calculate WER for Chinese audio. You can download it from here.
- wavlm_path: Path to the WavLM model, which is used to calculate speaker similarity. You can download it from here.
- eval_mode: Used to specify which version of the evaluation set to use. Choose between basic and open
- lang: supported language, choose between zh and en
If your directory for the edited audio is not organized in the format described above, you can run the following commands.
```
cd eval_scripts
# get wer, wer.noedit
bash cal_wer_edit.sh meta_file \
     wav_dir \
     lang \
     num_jobs \
     res_dir \
     task_type \
     eval_mode \
     whisper_path \
     paraformer_path \
     edit_cat # use `semantic` here
 # get sim
 bash cal_sim_edit.sh meta_file \
      wav_dir \
      wavlm_path \
      num_jobs \
      res_dir \
      lang
```
Here is a brief description of the parameters for the script above:
- meta_file: The absolute path to the meta file for the corresponding task (e.g., meta_en_deletion_basic.csv or meta_en_deletion.csv).
- wav_dir: The directory containing the edited audio files (the WAV files should be located directly in this directory).
- lang: zh or en
- num_jobs: number of process.
- res_dir: The directory to save the metric results.
- task_type: del, ins or sub
- eval_mode: The same as the above.
- whisper_path: The same as the above
- paraformer_path: The same as the above
- edit_cat: semantic or acoustic

Acoustic Editing

For the acoustic editing tasks, we use WER and SPK-SIM as the primary evaluation metrics.

If the directory for the edited audio is structured, you can run the following command.

cd Ming-Freeform-Audio-Edit/eval_scripts
bash run_eval_acoustic.sh eval_path \
                         whisper_path \
                         paraformer_path \
                         wavlm_path \
                         eval_mode \
                         lang

Otherwise, you can run commands similar to the one for the semantic tasks, with the edit_cat parameter set to acoustic.

Additionally, for the dialect and emotion conversion tasks, we assess the conversion accuracy by leveraging a large language model (LLM) through API calls, refer to eval_scripts/run_eval_acoustic.sh for more details.