Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Divehi
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use ptah23/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ptah23/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ptah23/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ptah23/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("ptah23/whisper-small-dv") - Notebooks
- Google Colab
- Kaggle
5000 epoch run
Browse files- .ipynb_checkpoints/README-checkpoint.md +1 -1
- README.md +1 -1
.ipynb_checkpoints/README-checkpoint.md
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dataset:
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name: Common Voice 13
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type: mozilla-foundation/common_voice_13_0
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split: None
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metrics:
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dataset:
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name: Common Voice 13
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type: mozilla-foundation/common_voice_13_0
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config: dv
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metrics:
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- name: Wer
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README.md
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@@ -18,7 +18,7 @@ model-index:
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dataset:
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name: Common Voice 13
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type: mozilla-foundation/common_voice_13_0
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config:
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split: None
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metrics:
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- name: Wer
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dataset:
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name: Common Voice 13
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type: mozilla-foundation/common_voice_13_0
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config: dv
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split: None
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metrics:
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- name: Wer
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