Update README.md

README.md

@@ -1,3 +1,92 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# README
+
+## Introduction
+This repository hosts Ming-Freeform-Audio-Edit, the benchmark test set for evaluating the downstream editing tasks of the Ming-UniAudio model.
+
+This test set covers 7 distinct editing tasks, categorized as follows:
+
++ 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
+| 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 |
+
+*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
+### 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)
+
+These metrics can be calculated by running the following command:
+```bash
+# run pip install -r requirements.txt first
+bash eval_scripts/semantic/run_eval.sh /path/contains/edited/audios
+```
+NOTE: the directory passed to the above script should have the structure as follows:
+```
+.
+├── del
+│   └── edit_del_basic
+│       ├── eval_result
+│       ├── hyp.txt
+│       ├── input_wavs
+│       ├── origin_wavs
+│       ├── ref.txt
+│       ├── test.jsonl
+│       ├── test_parse.jsonl # This is need to run the evaluation script
+│       ├── test.yaml
+│       └── tts/ # This is the directory contains the edited wavs
+```
+
+Examples of test_parse.jsonl:
+``` json
+{"uid": "00107947-00000092", "input_wav_path": "wavs/00107947-00000092.wav","output_wav_path": "edited_wavs/00107947-00000092.wav", "instruction": "Please recognize the language of this speech and transcribe it. And delete '随着经济的发'.\n", "asr_label": "随着经济的发展食物浪费也随之增长", "asr_text": "随着经济的发展食物浪费也随之增长", "edited_text_label": "展食物浪费也随之增长", "edited_text": "<edit></edit>展食物浪费也随之增长", "origin_speech_url": null,}
+
+{"uid": "00010823-00000019", "input_wav_path": "wavs/00010823-00000019.wav", "output_wav_path": "edited_wavs/00010823-00000019.wav", "instruction": "Please recognize the language of this speech and transcribe it. And delete the characters or words from index 4 to index 10.\n", "asr_label": "我们将为全球城市的可持续发展贡献力量", "asr_text": "我们将为全球城市的可持续发展贡献力量", "edited_text_label": "我们将持续发展贡献力量", "edited_text": "我们将<edit></edit>持续发展贡献力量", "origin_speech_url": null}
+```
+### Acoustic Editing
+For the acoustic editing tasks, we use WER and SPK-SIM as the primary evaluation metrics. These two metrics can be calculated by running the following commands:
+```bash
+bash eval_scripts/acoustic/cal_wer_sim.sh /path/contains/edited/audios
+```
+
+Additionally, for the dialect and emotion conversion tasks, we assess the conversion accuracy by leveraging a large language model (LLM) through API calls.
+```bash
+# dialect conversion accuracy
+python eval_scripts/acoustic/pyscripts/dialect_api.py --output_dir <保存评测结果的根目录> --generated_audio_dir <存放已生成音频文件的目录路径>
+# emotion conversion accuracy
+# fisrt, run: bash eval_scripts/acoustic/cal_wer_sim.sh /path/contains/edited/audios
+python pyscripts/emo_acc.py
+```