Whisper Hindi2Hinglish Swift ONNX
ONNX conversion of Oriserve/Whisper-Hindi2Hinglish-Swift for in-browser use with Transformers.js (dtype: 'q8', WASM/CPU friendly).
Fuente del modelo
- Tipo
- Text model
- Tamaño del archivo
- 75,7 MB
- Modelo base
- Oriserve/Whisper-Hindi2Hinglish-Swift
- Versión
- vdecoder_model_merged_quantized
- Actualizado
- 22 sept 2026
- Descargas
- 608
- Fuente
- Hugging Face
Descripción de la fuente
Whisper-Hindi2Hinglish-Swift (ONNX, q8)
ONNX conversion of Oriserve/Whisper-Hindi2Hinglish-Swift
for in-browser use with Transformers.js
(dtype: 'q8', WASM/CPU friendly).
The base model is openai/whisper-base (73M params) fine-tuned by
Oriserve on ~550 hours of noisy
Indian-accented Hindi. It transcribes Hindi-English code-mixed speech into
romanized Hinglish — English words stay in Latin script.
Conversion
- Exported with
optimum-cli export onnx --task automatic-speech-recognition-with-past - Quantized with onnxruntime dynamic quantization (QUInt8 weights,
EnableSubgraphfor the merged decoder), matching the layout Transformers.js expects fordtype: 'q8':onnx/encoder_model_quantized.onnx(23 MB)onnx/decoder_model_merged_quantized.onnx(79 MB)
Usage (Transformers.js)
import { pipeline } from '@huggingface/transformers';
const asr = await pipeline(
'automatic-speech-recognition',
'TechHirecentive/Whisper-Hindi2Hinglish-Swift-ONNX',
{ dtype: 'q8' },
);
const { text } = await asr(audioFloat32Array16kHz, {
language: 'hi',
task: 'transcribe',
});
License
Apache-2.0, same as the upstream fine-tune. All credit for the model weights to Oriserve; this repo only repackages them as quantized ONNX.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 22 sept
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoonnx/decoder_model_merged_quantized.onnx
onnx · 75,7 MB · SHA-256 ffab3719c07a…2cbe · Hugging Face
Download fileExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- license: apache-2.0 base_model: Oriserve/Whisper-Hindi2Hinglish-Swift pipeline_tag: automatic-speech-recognition library_name: transformers.js language: - hi - en tags: - whisper - hinglish - onnx - code-switching --- # Whisper-Hindi2Hinglish-Swift (ONNX, q8) ONNX conversion of [Oriserve/Whisper-Hindi2Hinglish-Swift](https://huggingface.co/Oriserve/Whisper-Hindi2Hinglish-Swift) for in-browser use with [Transformers.js](https://huggingface.co/docs/transformers.js) (`dtype: 'q8'`, WASM/CPU friendly). The base model is `openai/whisper-base` (73M params) fine-tuned by [Oriserve](https://huggingface.co/Oriserve) on ~550 hours of noisy Indian-accented Hindi. It transcribes Hindi-English code-mixed speech into **romanized Hinglish** — English words stay in Latin script. ## Conversion - Exported with `optimum-cli export onnx --task automatic-speech-recognition-with-past` - Quantized with onnxruntime dynamic quantization (QUInt8 weights, `EnableSubgraph` for the merged decoder), matching the layout Transformers.js expects for `dtype: 'q8'`: - `onnx/encoder_model_quantized.onnx` (23 MB) - `onnx/decoder_model_merged_quantized.onnx` (79 MB) ## Usage (Transformers.js) ```js import { pipeline } from '@huggingface/transformers'; const asr = await pipeline( 'automatic-speech-recognition', 'TechHirecentive/Whisper-Hindi2Hinglish-Swift-ONNX', { dtype: 'q8' }, ); const { text } = await asr(audioFloat32Array16kHz, { language: 'hi', task: 'transcribe', }); ``` ## License Apache-2.0, same as the upstream fine-tune. All credit for the model weights to [Oriserve](https://huggingface.co/Oriserve); this repo only repackages them as quantized ONNX.
Source context: 608 downloads · 0 likes · Pipeline automatic-speech-recognition · Library transformers.js · Repo Hirecentive-D3l/Whisper-Hindi2Hinglish-Swift-ONNX
