Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny") - Notebooks
- Google Colab
- Kaggle
Commit ·
4954caa
1
Parent(s): 8662dcd
add CV 11 hi results
Browse files
README.md
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@@ -139,6 +139,20 @@ model-index:
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- name: Test WER
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type: wer
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value: 17.15
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pipeline_tag: automatic-speech-recognition
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license: apache-2.0
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---
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- name: Test WER
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type: wer
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value: 17.15
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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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config: hi
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split: test
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args:
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language: hi
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metrics:
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- name: Test WER
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type: wer
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value: 141
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pipeline_tag: automatic-speech-recognition
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license: apache-2.0
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---
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