| --- |
| language: |
| - en |
| - zh |
| - de |
| - es |
| - ru |
| - ko |
| - fr |
| - ja |
| - pt |
| - tr |
| - pl |
| - ca |
| - nl |
| - ar |
| - sv |
| - it |
| - id |
| - hi |
| - fi |
| - vi |
| - he |
| - uk |
| - el |
| - ms |
| - cs |
| - ro |
| - da |
| - hu |
| - ta |
| - 'no' |
| - th |
| - ur |
| - hr |
| - bg |
| - lt |
| - la |
| - mi |
| - ml |
| - cy |
| - sk |
| - te |
| - fa |
| - lv |
| - bn |
| - sr |
| - az |
| - sl |
| - kn |
| - et |
| - mk |
| - br |
| - eu |
| - is |
| - hy |
| - ne |
| - mn |
| - bs |
| - kk |
| - sq |
| - sw |
| - gl |
| - mr |
| - pa |
| - si |
| - km |
| - sn |
| - yo |
| - so |
| - af |
| - oc |
| - ka |
| - be |
| - tg |
| - sd |
| - gu |
| - am |
| - yi |
| - lo |
| - uz |
| - fo |
| - ht |
| - ps |
| - tk |
| - nn |
| - mt |
| - sa |
| - lb |
| - my |
| - bo |
| - tl |
| - mg |
| - as |
| - tt |
| - haw |
| - ln |
| - ha |
| - ba |
| - jw |
| - su |
| tags: |
| - audio |
| - automatic-speech-recognition |
| - hf-asr-leaderboard |
| - open4bits |
| widget: |
| - example_title: Librispeech sample 1 |
| src: /static-proxy?url=https%3A%2F%2Fcdn-media.huggingface.co%2Fspeech_samples%2Fsample1.flac%3C%2Fspan%3E%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L110"> | | - example_title: Librispeech sample 2 |
| src: /static-proxy?url=https%3A%2F%2Fcdn-media.huggingface.co%2Fspeech_samples%2Fsample2.flac%3C%2Fspan%3E%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L112"> | | model-index: |
| - name: whisper-tiny |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: LibriSpeech (clean) |
| type: librispeech_asr |
| config: clean |
| split: test |
| args: |
| language: en |
| metrics: |
| - name: Test WER |
| type: wer |
| value: 7.54 |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: LibriSpeech (other) |
| type: librispeech_asr |
| config: other |
| split: test |
| args: |
| language: en |
| metrics: |
| - name: Test WER |
| type: wer |
| value: 17.15 |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: Common Voice 11.0 |
| type: mozilla-foundation/common_voice_11_0 |
| config: hi |
| split: test |
| args: |
| language: hi |
| metrics: |
| - name: Test WER |
| type: wer |
| value: 141 |
| pipeline_tag: automatic-speech-recognition |
| license: apache-2.0 |
| base_model: |
| - openai/whisper-tiny |
| --- |
| |
| # Open4bits / Whisper Tiny FP16 |
|
|
| This repository provides the **Whisper Tiny model converted to FP16 (float16) precision**, published by Open4bits to enable highly efficient inference with minimal memory usage. |
|
|
| The underlying Whisper model and architecture are **owned by OpenAI**. This repository contains only a precision-converted version of the original model weights. |
|
|
| The model is designed for fast, lightweight multilingual speech-to-text tasks and is well suited for resource-constrained environments. |
|
|
| --- |
|
|
| ## Model Overview |
|
|
| Whisper is a sequence-to-sequence transformer model developed by OpenAI for automatic speech recognition and speech translation. |
| This release uses the **Tiny** variant, prioritizing speed and low memory usage while preserving the original architecture. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| - **Architecture:** Whisper Tiny |
| - **Parameters:** ~37.85 million |
| - **Precision:** float16 (FP16) |
| - **Task:** Automatic Speech Recognition (ASR) |
| - **Languages:** Multilingual |
| - **Weight tying:** Preserved |
| - **Compatibility:** Hugging Face Transformers, PyTorch |
|
|
| Compared to larger Whisper variants, this model offers significantly faster inference and lower VRAM requirements, with reduced accuracy in some scenarios. |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| This model is intended for: |
| - Fast speech-to-text transcription |
| - Lightweight and real-time ASR applications |
| - Edge or low-resource deployments |
| - Research and prototyping |
|
|
| --- |
|
|
| ## Limitations |
|
|
| * Lower transcription accuracy compared to larger Whisper variants |
| * Performance depends on audio quality, language, and accent |
| * Not fine-tuned for domain-specific or noisy audio |
|
|
| --- |
|
|
| ## License |
|
|
| This model is released under the **Apache License 2.0**. |
| The original Whisper model and associated intellectual property are owned by OpenAI. |
|
|
| --- |
|
|
| ## Support |
|
|
| If you find this model useful, please consider supporting the project. |
| Your support helps us continue releasing and maintaining high-quality open models. |
| Support us with a heart. |
