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Whisper Hindi2Hinglish Swift ONNX

ONNX conversion of Oriserve/Whisper-Hindi2Hinglish-Swift for in-browser use with Transformers.js (dtype: 'q8', WASM/CPU friendly).

KindText modelBase modelOriserve/Whisper-Hindi2Hinglish-SwiftVersion

Model source

Kind
Text model
File size
75.7 MB
Base model
Oriserve/Whisper-Hindi2Hinglish-Swift
Version
vdecoder_model_merged_quantized
Updated
Sep 22, 2026
Downloads
608
Source
Hugging Face

Source description

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, 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)

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.

Sources

1 source
Hugging FacePrimaryVersion 24211661105a7bb9e96a170fb92db173667e31fd
huggingface.co/Hirecentive-D3l/Whisper-Hindi2Hinglish-Swift-ONNX/tree/24211661105a7bb9e96a170fb92db173667e31fd

Verified Sep 22

Model artifacts

1 artifact

onnx/decoder_model_merged_quantized.onnx

onnx · 75.7 MB · SHA-256 ffab3719c07a…2cbe · Hugging Face

Download file

Source excerpts

2 excerpts

--- 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