Update README.md

README.md

@@ -24,13 +24,21 @@ The audio samples are sourced from well-known open-source datasets, including se
 
 ## 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 |
 
-*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)
+#### 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
@@ -42,6 +50,13 @@ The audio samples are sourced from well-known open-source datasets, including se
 | Emotion Conversion               |   84 |   72 |
 | Volume Conversion                |   50 |   50 |
 ## Evaluation Metrics
+### Environment Preparation
+```bash
+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](https://huggingface.co/datasets/inclusionAI/Ming-Freeform-Audio-Edit-Benchmark/tree/main) or [ModelScope](https://modelscope.cn/datasets/inclusionAI/Ming-Freeform-Audio-Edit-Benchmark/files) 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)
@@ -49,44 +64,86 @@ For the deletion, insertion, and substitution tasks, we evaluate the performance
 + 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
-```
+1. 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:
+   ```bash
+   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](https://huggingface.co/openai/whisper-large-v3).
+     + `paraformer_path`:Path to the Paraformer model, which is used to calculate WER for Chinese audio. You can download it from [here](https://huggingface.co/funasr/paraformer-zh).
+     + `wavlm_path`: Path to the WavLM model, which is used to calculate speaker similarity. You can download it from [here](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view).
+     + `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`
+
+2. If your directory for the edited audio is not organized in the format described above, you can run the following commands.
+   ```bash
+   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`
 
-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
-```
+For the acoustic editing tasks, we use WER and SPK-SIM as the primary evaluation metrics. 
 
-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
-```
+1. If the directory for the edited audio is structured, you can run the following command.
+   ```bash
+   cd Ming-Freeform-Audio-Edit/eval_scripts
+   bash run_eval_acoustic.sh eval_path \
+                            whisper_path \
+                            paraformer_path \
+                            wavlm_path \
+                            eval_mode \
+                            lang
+   ```
+2. 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.