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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-tiny |
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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: 7.54 |
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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: 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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base_model: |
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- openai/whisper-tiny |
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--- |
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# Open4bits / Whisper Tiny FP16 |
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This repository provides the **Whisper Tiny model converted to FP16 (float16) precision**, published by Open4bits to enable highly efficient inference with minimal memory usage. |
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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 fast, lightweight multilingual speech-to-text tasks and is well suited for resource-constrained environments. |
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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 **Tiny** variant, prioritizing speed and low memory usage while preserving the original architecture. |
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--- |
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## Model Details |
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- **Architecture:** Whisper Tiny |
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- **Parameters:** ~37.85 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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Compared to larger Whisper variants, this model offers significantly faster inference and lower VRAM requirements, with reduced accuracy in some scenarios. |
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--- |
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## Intended Use |
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This model is intended for: |
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- Fast speech-to-text transcription |
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- Lightweight and real-time ASR applications |
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- Edge or low-resource deployments |
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- Research and prototyping |
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--- |
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## Limitations |
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* Lower transcription accuracy compared to larger Whisper variants |
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* Performance depends on audio quality, language, and accent |
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* Not fine-tuned for domain-specific or 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. |
