Automatic Speech Recognition
Transformers
PyTorch
Thai
wav2vec2
audio
hf-asr-leaderboard
robust-speech-event
speech
xlsr-fine-tuning
Eval Results (legacy)
Instructions to use airesearch/wav2vec2-large-xlsr-53-th with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use airesearch/wav2vec2-large-xlsr-53-th with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="airesearch/wav2vec2-large-xlsr-53-th")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") model = AutoModelForCTC.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesadd deepcut benchmark
README.md
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## Evaluation
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We benchmark on the test set using WER with words tokenized by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) 2.3.1 and CER. We also measure performance when spell correction using [TNC](http://www.arts.chula.ac.th/ling/tnc/) ngrams is applied. Evaluation codes can be found in `notebooks/wav2vec2_finetuning_tutorial.ipynb`. Benchmark is performed on `test-unique` split.
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| Ours without spell correction
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| Ours with spell correction
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※ APIs are not finetuned with Common Voice 7.0 data
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## Evaluation
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We benchmark on the test set using WER with words tokenized by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) 2.3.1 and [deepcut](https://github.com/rkcosmos/deepcut), and CER. We also measure performance when spell correction using [TNC](http://www.arts.chula.ac.th/ling/tnc/) ngrams is applied. Evaluation codes can be found in `notebooks/wav2vec2_finetuning_tutorial.ipynb`. Benchmark is performed on `test-unique` split.
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| | WER PyThaiNLP 2.3.1 | WER deepcut | CER |
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| Ours without spell correction | 0.13634024 | **0.08152052** | **0.02813019** |
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| Ours with spell correction | 0.17996397 | 0.14167975 | 0.05225761 |
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| Google Web Speech API※ | 0.13711234 | 0.10860058 | 0.07357340 |
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| Microsoft Bing Speech API※ | **0.12578819** | 0.09620991 | 0.05016620 |
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| Amazon Transcribe※ | 0.2186334 | 0.14487553 | 0.07077562 |
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| NECTEC AI for Thai Partii API※ | 0.20105887 | 0.15515631 | 0.09551027 |
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※ APIs are not finetuned with Common Voice 7.0 data
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