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readme update

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@@ -183,9 +183,44 @@ img {
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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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- ## Transformers
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- You can now run Parakeet CTC natively with [Transformers](https://github.com/huggingface/transformers) 🤗
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  ```bash
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  pip install git+https://github.com/huggingface/transformers
@@ -254,33 +289,7 @@ outputs.loss.backward()
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  ```
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  </details>
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- ## NVIDIA NeMo: Training
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-
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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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-
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- ## How to Use this Model
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-
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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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-
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- ### Automatically instantiate the model
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-
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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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-
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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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+
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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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+
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+ ## How to Use this Model
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+
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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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+
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+ ### Automatically instantiate the model
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+
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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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+
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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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+
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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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+
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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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