Automatic Speech Recognition
Transformers
PyTorch
Marathi
wav2vec2
speech_to_text
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use Tejas2000/SpeechRecog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tejas2000/SpeechRecog with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Tejas2000/SpeechRecog")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Tejas2000/SpeechRecog") model = AutoModelForCTC.from_pretrained("Tejas2000/SpeechRecog", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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license: apache-2.0
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datasets:
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- openslr
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language:
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- mr
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metrics:
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- wer
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pipeline_tag: text-generation
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tags:
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- speech_to_text
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- audio
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- automatic-speech-recognition
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- speech
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- xlsr-fine-tuning-week
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model-index:
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- name: XLSR Wav2Vec2 Large 53 Marathi by Sumedh Khodke
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results:
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: OpenSLR mr
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type: openslr
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metrics:
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- name: Test WER
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type: wer
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value: 12.7
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---
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