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atariq701/
mms-1b-allFT-urdu-asr-csalt-voice-urd-script_arabic

Name: mms-1b-allFT-urdu-asr-csalt-voice-urd-script_arabic
Availability: InStock

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MMS Fine-tuned model for urd-script_arabic

This model is a fine-tuned version of facebook/mms-1b-all on the urdu-asr/csalt-voice dataset.

Model description

This is an adapter-based fine-tuned model that uses the MMS-1B model as the base and only trains language-specific adapters for Urdu speech recognition. The model uses Urdu written in Arabic script.

Training and evaluation

Training Data: urdu-asr/csalt-voice dataset
Training Time: Approximately 3.7 hours
Final Word Error Rate (WER): 41.92%
Final Loss: 0.9216
Training Method: Adapter-based fine-tuning with frozen base model

The model was trained for 98.11 epochs, with the best model saved at the end.

Usage

python

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import librosa

# Load model & processor
processor = Wav2Vec2Processor.from_pretrained("atariq701/mms-1b-allFT-urdu-asr-csalt-voice-urd-script_arabic")
model = Wav2Vec2ForCTC.from_pretrained("atariq701/mms-1b-allFT-urdu-asr-csalt-voice-urd-script_arabic")

# Load audio (ensure 16kHz sampling rate)
audio_input, sample_rate = librosa.load("path/to/audio.wav", sr=16000)

# Process audio
inputs = processor(audio_input, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    logits = model(inputs.input_values).logits

# Decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(transcription)