File size: 7,372 Bytes
828d8d5 c87ba14 828d8d5 c87ba14 828d8d5 c87ba14 828d8d5 c87ba14 828d8d5 c87ba14 828d8d5 c87ba14 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
---
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
#### 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
```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)
+ Word Error Rate (WER) of the Non-edited Region (wer.noedit)
+ Edit Operation Accuracy (acc)
+ Speaker Similarity (sim)
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`
### Acoustic Editing
For the acoustic editing tasks, we use WER and SPK-SIM as the primary evaluation metrics.
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.
|