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--- |
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language: |
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- ur |
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library_name: nemo |
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datasets: |
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- mozilla-foundation/common_voice_12_0 |
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thumbnail: null |
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tags: |
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- automatic-speech-recognition |
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- speech |
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- audio |
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- Transducer |
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- FastConformer |
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- Conformer |
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- pytorch |
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- NeMo |
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license: cc-by-4.0 |
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widget: |
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- Title: Common Voice Urdu Sample |
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src: https://cdn-media.huggingface.co/speech_samples/sample_urdu.flac |
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model-index: |
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- name: parakeet-rnnt-0.6b-urdu |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Mozilla Common Voice 12.0 (Urdu) |
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type: mozilla-foundation/common_voice_12_0 |
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split: test |
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args: |
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language: ur |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 25.513 |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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--- |
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# Fine-Tuned Parakeet RNNT 0.6B (Urdu) |
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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. |
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--- |
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## Model Overview |
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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. |
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Base model details can be found on 🤗 [Hugging Face](https://huggingface.co/nvidia/parakeet-rnnt-0.6b). |
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--- |
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## Training Details |
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### Dataset |
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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. |
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### Hardware |
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- **Google Colab Pro** |
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- **NVIDIA A100 GPU** |
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--- |
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## Results |
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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: |
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- **Reference**: کچھ بھی ہو سکتا ہے۔ |
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**Predicted**: کچھ بھی ہو سکتا ہے۔ |
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--- |
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- **Reference**: اورکوئی جمہوریت کو کوس رہا ہے۔ |
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**Predicted**: اور کوئ جمہوریت کو کو س رہا ہے۔ |
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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. |
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--- |
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## How to Use this Model |
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### Loading the Model |
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You can load the fine-tuned model using NVIDIA NeMo: |
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```python |
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import nemo.collections.asr as nemo_asr |
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asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="hash2004/parakeet-fine-tuned-urdu") |
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``` |
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## How to Fine Tune this Model |
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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). |
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