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README.md
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It is an XL version of FastConformer CTC [1] (around 600M parameters) model.
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See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) for complete architecture details.
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##
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```bash
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pip install git+https://github.com/huggingface/transformers
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```
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</details>
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To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
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```
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pip install nemo_toolkit['all']
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```
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## How to Use this Model
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The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
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### Automatically instantiate the model
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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.from_pretrained(model_name="nvidia/parakeet-ctc-0.6b")
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```
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### Transcribing using Python
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First, let's get a sample
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```
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wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
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```
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Then simply do:
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```
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asr_model.transcribe(['2086-149220-0033.wav'])
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```
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### Transcribing many audio files
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It is an XL version of FastConformer CTC [1] (around 600M parameters) model.
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See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) for complete architecture details.
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## NVIDIA NeMo: Training
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To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
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```
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pip install nemo_toolkit['all']
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```
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## How to Use this Model
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The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. Moreover, you can now run Parakeet CTC natively with [Transformers](https://github.com/huggingface/transformers) 🤗.
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### Automatically instantiate the model
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Using [NVIDIA NeMo](https://github.com/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.EncDecCTCModelBPE.from_pretrained(model_name="nvidia/parakeet-ctc-0.6b")
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```
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Using [Transformers](https://github.com/huggingface/transformers) 🤗
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```python
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from transformers import AutoModelForCTC
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model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-0.6b")
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```
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### Transcribing using NeMo
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First, let's get a sample
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```
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wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
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```
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Then simply do:
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```
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asr_model.transcribe(['2086-149220-0033.wav'])
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```
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### Transcribing using [Transformers](https://github.com/huggingface/transformers) 🤗
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Make sure to install `transformers` from source.
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```bash
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pip install git+https://github.com/huggingface/transformers
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```
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</details>
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For more details about usage, the refer to [Transformers' documentation](https://huggingface.co/docs/transformers/en/index).
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### Transcribing many audio files
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