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--- |
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license: mit |
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language: |
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- bn |
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metrics: |
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- wer |
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- cer |
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library_name: nemo |
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pipeline_tag: automatic-speech-recognition |
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--- |
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#### Model |
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**Conformer-CTC** model trained on the *OOD-Speech dataset* to transcribe speech from Bangla audio. This is a large variant of the model, with ~121M parameters. To know more about the model architecture see the NeMo Documentation [here](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc). |
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#### Dataset |
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The training split contains `1100+ hours` of audio data crowdsoruced from native Bangla speakers. We trained on this split for `164 epochs` , then the model was evaluated on`23+ hours` of audio across 17 diverse domains, with a validation score of `22.4% WER` . |
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#### Usage |
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The model can be used as a pretrained checkpoint for inference or for fine-tuning on another dataset through the [NVIDIA NeMo toolkit](https://github.com/NVIDIA/NeMo). It is recommended to install the toolkit, after installing the pyTorch package. |
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```bash |
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apt-get update && apt-get install -y libsndfile1 ffmpeg sox |
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pip install Cython |
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pip install nemo_toolkit['all'] |
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pip uninstall -y torchmetrics |
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pip install torchmetrics==0.9.2 |
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``` |
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After installing the required dependencies, download the .nemo file or the pretrained model to your local directory. you can instantiate the pretrained model like following: |
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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.EncDecCTCModelBPE.restore_from(restore_path="<MODEL PATH>") |
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``` |
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##### Data Preprocessing |
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Prior to feeding the input audio to the pretrained model for inference, we need to resample the audio to **16KHz**. We can achieve that using the `sox` library : |
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```python |
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from sox import Transformer |
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if not os.path.exists("<RESAMPLED AUDIO FILE PATH>"): |
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tfm = Transformer() |
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tfm.rate(samplerate=16000) |
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tfm.channels(n_channels=1) |
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tfm.build(input_filepath= "<AUDIO FILE PATH>", output_filepath= "<RESAMPLED AUDIO FILE PATH>") |
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``` |
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##### Inference using the library |
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```python |
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asr_model.transcribe(paths2audio_files="<LIST OF RESAMPLED AUDIO FILES>") |
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``` |
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##### Infer an entire batch of audio files |
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```bash |
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python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ |
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model_path="<PRETRAINED MODEL PATH>" \ |
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audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" \ |
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output_filename="<PATH TO OUTPUT JSON>" \ |
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batch_size=1 |
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``` |
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#### Performance |
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The model has been separately evaluated across the different domain distributions of the test data. |
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For more details please refer this paper: [OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking](https://arxiv.org/pdf/2305.09688.pdf) |
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If you this model in your work, please cite the following paper : |
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``` |
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@misc{rakib2023oodspeech, |
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title={OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking}, |
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author={Fazle Rabbi Rakib and Souhardya Saha Dip and Samiul Alam and Nazia Tasnim and Md. Istiak Hossain Shihab and Md. Nazmuddoha Ansary and Syed Mobassir Hossen and Marsia Haque Meghla and Mamunur Mamun and Farig Sadeque and Sayma Sultana Chowdhury and Tahsin Reasat and Asif Sushmit and Ahmed Imtiaz Humayun}, |
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year={2023}, |
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eprint={2305.09688}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.AS} |
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} |
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``` |
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