Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
multilingual
whisper
Generated from Trainer
Instructions to use fayez94/whisper-medium_AcodeMixed_ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fayez94/whisper-medium_AcodeMixed_ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="fayez94/whisper-medium_AcodeMixed_ASR")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("fayez94/whisper-medium_AcodeMixed_ASR") model = AutoModelForSpeechSeq2Seq.from_pretrained("fayez94/whisper-medium_AcodeMixed_ASR") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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license: apache-2.0
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base_model: openai/whisper-medium
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tags:
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# Whisper Medium CodeMixed bn-en - Mohammad Fayez Ullah
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the
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It achieves the following results on the evaluation set:
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- eval_loss: 0.1913
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- eval_wer: 40.7018
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---
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language:
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- multilingual
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license: apache-2.0
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base_model: openai/whisper-medium
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tags:
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# Whisper Medium CodeMixed bn-en - Mohammad Fayez Ullah
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Custom dataset: CodeMixed-BanEng dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 0.1913
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- eval_wer: 40.7018
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