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--- |
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language: |
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- en |
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- zh |
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- de |
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- es |
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- ru |
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- ko |
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- fr |
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- ja |
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- pt |
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- tr |
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- pl |
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- ca |
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- nl |
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- ar |
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- sv |
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- it |
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- id |
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- hi |
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- fi |
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- vi |
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- he |
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- uk |
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- el |
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- ms |
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- cs |
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- ro |
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- da |
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- hu |
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- ta |
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- 'no' |
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- th |
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- ur |
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- hr |
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- bg |
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- lt |
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- la |
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- mi |
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- ml |
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- cy |
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- sk |
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- te |
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- fa |
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- lv |
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- bn |
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- sr |
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- az |
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- sl |
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- kn |
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- et |
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- mk |
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- br |
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- eu |
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- is |
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- hy |
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- ne |
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- mn |
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- bs |
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- kk |
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- sq |
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- sw |
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- gl |
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- mr |
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- pa |
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- si |
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- km |
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- sn |
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- yo |
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- so |
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- af |
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- oc |
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- ka |
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- be |
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- tg |
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- sd |
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- gu |
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- am |
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- yi |
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- lo |
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- uz |
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- fo |
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- ht |
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- ps |
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- tk |
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- nn |
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- mt |
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- sa |
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- lb |
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- my |
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- bo |
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- tl |
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- mg |
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- as |
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- tt |
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- haw |
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- ln |
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- ha |
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- ba |
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- jw |
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- su |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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- open4bits |
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widget: |
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- example_title: Librispeech sample 1 |
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src: https://cdn-media.huggingface.co/speech_samples/sample1.flac |
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- example_title: Librispeech sample 2 |
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src: https://cdn-media.huggingface.co/speech_samples/sample2.flac |
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model-index: |
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- name: whisper-base |
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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: LibriSpeech (clean) |
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type: librispeech_asr |
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config: clean |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 5.008769117619326 |
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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: LibriSpeech (other) |
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type: librispeech_asr |
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config: other |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 12.84936273212057 |
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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: 131 |
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pipeline_tag: automatic-speech-recognition |
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license: apache-2.0 |
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base_model: |
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- openai/whisper-base |
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--- |
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# Open4bits / Whisper Base FP16 |
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This repository provides the **Whisper Base model converted to FP16 (float16) precision**, published by Open4bits to enable more efficient inference while maintaining transcription quality. |
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The underlying Whisper model and architecture are **owned by OpenAI**. This repository contains only a precision-converted version of the original model weights. |
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The model is designed for multilingual speech-to-text tasks and can be used in research, experimentation, and production ASR pipelines. |
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--- |
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## Model Overview |
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Whisper is a sequence-to-sequence transformer model developed by OpenAI for automatic speech recognition and speech translation. |
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This release uses the **Base** variant and preserves the original architecture while reducing memory usage through FP16 precision. |
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--- |
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## Model Details |
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- **Architecture:** Whisper Base |
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- **Parameters:** ~74 million |
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- **Precision:** float16 (FP16) |
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- **Task:** Automatic Speech Recognition (ASR) |
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- **Languages:** Multilingual |
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- **Weight tying:** Preserved |
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- **Compatibility:** Hugging Face Transformers, PyTorch |
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This conversion improves inference speed and lowers VRAM requirements compared to FP32 versions, making it suitable for deployment on consumer and server-grade GPUs. |
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--- |
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## Intended Use |
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This model is intended for: |
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- Speech-to-text transcription |
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- Multilingual ASR applications |
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- Research and benchmarking |
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- Efficient inference in low-memory environments |
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--- |
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## Limitations |
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* Performance depends on audio quality, language, and accent |
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* Inherits known limitations of the Whisper Base architecture |
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* Not fine-tuned for domain-specific or highly noisy audio |
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--- |
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## License |
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This model is released under the **Apache License 2.0**. |
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The original Whisper model and associated intellectual property are owned by OpenAI. |
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--- |
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## Support |
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If you find this model useful, please consider supporting the project. |
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Your support helps us continue releasing and maintaining high-quality open models. |
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Support us with a heart. |
