Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hindi
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use arbml/whisper-tiny-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbml/whisper-tiny-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arbml/whisper-tiny-ar")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arbml/whisper-tiny-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("arbml/whisper-tiny-ar") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
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@@ -9,20 +9,21 @@ datasets:
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- mozilla-foundation/common_voice_11_0
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metrics:
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- wer
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model-index:
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- name: Whisper Small Hi - Sanchit Gandhi
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice 11.0
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type: mozilla-foundation/common_voice_11_0
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args: 'config: hi, split: test'
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metrics:
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type: wer
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value: 83.4696132596685
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- mozilla-foundation/common_voice_11_0
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metrics:
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- wer
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base_model: openai/whisper-small
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model-index:
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- name: Whisper Small Hi - Sanchit Gandhi
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: Common Voice 11.0
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type: mozilla-foundation/common_voice_11_0
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args: 'config: hi, split: test'
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metrics:
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- type: wer
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value: 83.4696132596685
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name: Wer
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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