Automatic Speech Recognition
Transformers
PyTorch
JAX
English
wav2vec2
audio
hf-asr-leaderboard
mozilla-foundation/common_voice_6_0
robust-speech-event
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use abidlabs/speech-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abidlabs/speech-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="abidlabs/speech-text")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("abidlabs/speech-text") model = AutoModelForCTC.from_pretrained("abidlabs/speech-text") - Notebooks
- Google Colab
- Kaggle
Commit ·
5eca7b2
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Parent(s): a005325
Update README.md
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README.md
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@@ -43,12 +43,12 @@ The script used for training can be found here: https://github.com/jonatasgrosma
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The model can be used directly (without a language model) as follows...
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Using the [
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```python
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from asrecognition import ASREngine
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asr = ASREngine("en")
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audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
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transcriptions = asr.transcribe(audio_paths)
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The model can be used directly (without a language model) as follows...
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Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library:
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```python
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from asrecognition import ASREngine
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asr = ASREngine("en", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-english")
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audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
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transcriptions = asr.transcribe(audio_paths)
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