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Saidakbar01/
whisper-base-uz-onnx-int8

Name: whisper-base-uz-onnx-int8
Availability: InStock

automatic-speech-recognition

apache-2.0

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whisper-base · INT8 ONNX — Uzbek Speech Recognition

ONNX export of Saidakbar01/whisper-base-uz-finetuned, a Whisper-base model fine-tuned for the Uzbek language.

Quantized with INT8 dynamic quantization (AutoQuantizationConfig.avx2, is_static=False, per_channel=False).

Model Details

Property	Value
Base architecture	openai/whisper-base
Fine-tuned model	Saidakbar01/whisper-base-uz-finetuned
Format	ONNX (INT8)
Size	170 MB
WER (200-sample validation)	56.31%
Language	Uzbek (uz)
Task	Automatic Speech Recognition

Files

encoder_model_quantized.onnx
decoder_model_quantized.onnx
decoder_with_past_model_quantized.onnx

Usage

python

from optimum.onnxruntime import ORTModelForSpeechSeq2Seq
from transformers import WhisperProcessor
import torch

# Load processor and ONNX model
processor = WhisperProcessor.from_pretrained(
    "openai/whisper-base",
    language="uz",
    task="transcribe",
)
model = ORTModelForSpeechSeq2Seq.from_pretrained(
    "Saidakbar01/whisper-base-uz-onnx-int8",
    encoder_file_name="encoder_model_quantized.onnx",
    decoder_file_name="decoder_model_quantized.onnx",
    decoder_with_past_file_name="decoder_with_past_model_quantized.onnx",
)

# Transcribe audio (numpy float32 array at 16 kHz)
inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    predicted_ids = model.generate(
        inputs.input_features,
        language="uz",
        task="transcribe",
    )
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(transcription)

Training & Evaluation

The parent model was fine-tuned on the DavronSherbaev/uzbekvoice-filtered dataset. WER was measured on 200 samples from the validation split using jiwer.

Model	WER
openai/whisper-base (baseline)	200.69%
Saidakbar01/whisper-base-uz-finetuned (fine-tuned)	53.13%
whisper-base-uz-onnx-int8 (this model)	56.31%