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hash2004
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parakeet-fine-tuned-urdu

Automatic Speech Recognition
NeMo
PyTorch
Urdu
speech
audio
Transducer
FastConformer
Conformer
NeMo
Eval Results
Model card Files Community
parakeet-fine-tuned-urdu
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---
language:
- ur
library_name: nemo
datasets:
- mozilla-foundation/common_voice_12_0
thumbnail: null
tags:
- automatic-speech-recognition
- speech
- audio
- Transducer
- FastConformer
- Conformer
- pytorch
- NeMo
license: cc-by-4.0
widget:
- Title: Common Voice Urdu Sample
  src: https://cdn-media.huggingface.co/speech_samples/sample_urdu.flac
model-index:
- name: parakeet-rnnt-0.6b-urdu
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Mozilla Common Voice 12.0 (Urdu)
      type: mozilla-foundation/common_voice_12_0
      split: test
      args:
        language: ur
    metrics:
    - name: Test WER
      type: wer
      value: 25.513
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---
# Fine-Tuned Parakeet RNNT 0.6B (Urdu)

This repository contains the fine-tuned version of the **Parakeet RNNT 0.6B** model for **Urdu** Automatic Speech Recognition (ASR). The base model, developed by **NVIDIA NeMo** and **Suno.ai**, was fine-tuned on the Urdu dataset from Mozilla's Common Voice 12.0. This fine-tuning enables the model to perform speech-to-text tasks in Urdu with improved accuracy and domain-specific adaptation.

---

## Model Overview

The **Parakeet RNNT** is an XL version of the FastConformer Transducer with **600 million parameters**, optimized for ASR tasks. The fine-tuned model supports Urdu transcription, enabling applications such as subtitling, speech analytics, and voice-assisted interfaces.

Base model details can be found on 🤗 [Hugging Face](https://huggingface.co/nvidia/parakeet-rnnt-0.6b).

---

## Training Details

### Dataset
The fine-tuning was performed using the **Urdu dataset** from Mozilla's [Common Voice 12.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_12_0). This dataset provides diverse speech samples in Urdu, ensuring robust training.

### Hardware
- **Google Colab Pro**
- **NVIDIA A100 GPU**

---

## Results

The model achieved a **Word Error Rate (WER)** of **25.513%** on the test split of the Common Voice Urdu dataset. While this may seem high, the model demonstrates impressive accuracy in many transcriptions:

- **Reference**: کچھ بھی ہو سکتا ہے۔  
  **Predicted**: کچھ بھی ہو سکتا ہے۔  

---

- **Reference**: اورکوئی جمہوریت کو کوس رہا ہے۔  
  **Predicted**: اور کوئ جمہوریت کو  کو س رہا ہے۔  

This WER is slightly higher than OpenAI's **Whisper model**, which achieved **23%** without fine-tuning ([reference](https://arxiv.org/html/2409.11252v1)), but demonstrates the potential of the Parakeet RNNT with further fine-tuning.

---

## How to Use this Model

### Loading the Model

You can load the fine-tuned model using NVIDIA NeMo:

```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="hash2004/parakeet-fine-tuned-urdu")
```

## How to Fine Tune this Model 
You can find all resources on fine-tuning the Parakeet RNNT (0.6B) model on [this GitHub Repository](https://github.com/hash2004/conformer-fine-tuned-urdu).