Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Bashkir
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use stdbug/whisper-base-ba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stdbug/whisper-base-ba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="stdbug/whisper-base-ba")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("stdbug/whisper-base-ba") model = AutoModelForSpeechSeq2Seq.from_pretrained("stdbug/whisper-base-ba") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files
README.md
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- name: Whisper base bashkir
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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 17.0 (ba)
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type: stdbug/common-voice-17-ba
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args: 'config: ba, split: test'
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metrics:
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type: wer
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value: 35.15895985683671
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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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- name: Whisper base bashkir
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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 17.0 (ba)
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type: stdbug/common-voice-17-ba
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args: 'config: ba, split: test'
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metrics:
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- type: wer
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value: 35.15895985683671
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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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