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Keyword spotting and forced alignment in any language

clap-ipa 최신버젼 다운로드

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// repository documentation

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# CLAP-IPA
[The taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language](https://aclanthology.org/2024.naacl-long.43). NAACL 2024.   
   
### Usage

See `kws_example.ipynb` and `forced_alignment_example.ipynb`  for a comprehensive example.

#### Install
```
git clone https://github.com/lingjzhu/clap-ipa
cd clap-ipa
pip install .
```

#### Inference

For CLAP-IPA
```python
from clap.encoders import *
import torch.nn.functional as F
from transformers import DebertaV2Tokenizer, AutoProcessor

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

speech_encoder = SpeechEncoder.from_pretrained('anyspeech/clap-ipa-tiny-speech')
phone_encoder = PhoneEncoder.from_pretrained('anyspeech/clap-ipa-tiny-phone')
phone_encoder.eval().to(device)
speech_encoder.eval().to(device)

tokenizer = DebertaV2Tokenizer.from_pretrained('charsiu/IPATokenizer')
processor = AutoProcessor.from_pretrained('openai/whisper-tiny')

audio_input = processor(some_audio)
ipa_input = tokenizer(some_ipa_string)

with torch.no_grad():
   speech_embed = speech_encoder(audio_input)
   phone_embed = phone_encoder(ipa_input)

similarity = F.cosine_similarity(speech_embed,phone_embed,dim=-1)
```

For IPA-Aligner, the example usage is in `forced_alignment_example.ipynb`.
The full forced-alignment evaluation code is in `evaluate/eval_boundary.py`.

#### Training
For training, you can download data from HuggingFace hub. Then sample train/val filelists are available in `data/`. 
```
python train.py -c config/clap_ipa/base.yaml
```

#### Evaluation
Evaluation code is available in `evaluate`. Each evalaute code script has almost the same organization, so you can simply pass the `.ckpt` checkpoint after training to evaluate their performance. Please check the evalaution code for usage.
```
python evaluate_fieldwork.py --data ucla --checkpoint "last.ckpt"
```

### Pretrained Models
Weights are released under [MIT License](https://github.com/lingjzhu/clap-ipa?tab=MIT-1-ov-file#MIT-1-ov-file).
| Model | Phone Encoder | Speech encoder |
|---|---|---|
| CLAP-IPA-tiny | `anyspeech/clap-ipa-tiny-phone` | `anyspeech/clap-ipa-tiny-speech` |
| CLAP-IPA-base | `anyspeech/clap-ipa-base-phone` | `anyspeech/clap-ipa-base-speech` |
| CLAP-IPA-small | `anyspeech/clap-ipa-small-phone` | `anyspeech/clap-ipa-small-speech` |
| IPA-Aligner-tiny | `anyspeech/ipa-align-tiny-phone` | `anyspeech/ipa-align-tiny-speech` |
| IPA-Aligner-base | `anyspeech/ipa-align-base-phone` | `anyspeech/ipa-align-base-speech` |
| IPA-Aligner-small | `anyspeech/ipa-align-small-phone` | `anyspeech/ipa-align-base-speech` |

### IPA Pack
All datasets are distributed as `wds` files on huggingface hub.   
 - **FLEURS-IPA**: https://huggingface.co/datasets/anyspeech/fleurs_ipa
 - **MSWC-IPA**: https://huggingface.co/datasets/anyspeech/mswc_ipa
 - **DORECO-IPA**: https://huggingface.co/datasets/anyspeech/doreco_ipa

After this study, we found that these datasets still contain inconsistent unicode encoding of IPA symbols.  
**A cleaner version will be released when we finish another round of data cleaning**.
The clean data (~1.8TB) and the models trained on clean data are available at: [https://github.com/lingjzhu/zipa](https://github.com/lingjzhu/zipa)

#### To download these datasets:
```python
from huggingface_hub import snapshot_download

snapshot_download(repo_id="anyspeech/fleurs_ipa", repo_type="dataset", local_dir="your_own_folder",local_dir_use_symlinks=False,resume_download=False,max_workers=4)

```

#### To load webdataset files:
```python
import webdataset as wds  # Note the typical import shorthand
dataset = (
      wds.WebDataset("data-archives/shard-00{00...24}.tar")  # 25 shards
      .decode()  # Automagically decode files
      .shuffle(size=1000)  # Shuffle on-the-fly in a buffer
      .batch(batchsize=10)  # Create batches
)
```

### Citation
```
@inproceedings{zhu-etal-2024-taste,
    title = "The taste of {IPA}: Towards open-vocabulary keyword spotting and forced alignment in any language",
    author = "Zhu, Jian  and
      Yang, Changbing  and
      Samir, Farhan  and
      Islam, Jahurul",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-long.43/",
    doi = "10.18653/v1/2024.naacl-long.43",
    pages = "750--772"
}
```

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