parakeet-ctc

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parakeet-ctc-1.1b is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by NVIDIA NeMo and Suno.ai teams. It is an XXL version of FastConformer CTC [1] (around 1.1B parameters) model. See the model architecture section and NeMo documentation for complete architecture details.

NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest PyTorch version.

pip install nemo_toolkit['all']

How to Use this Model

There are several ways to use this model. Choose the one that fits your needs.

Run locally with NeMo-Speech.cpp

NeMo-Speech.cpp provides a lightweight native C++ runtime for local inference with this model. After installing the runtime:

hf download nvidia/parakeet-ctc-1.1b \
  parakeet-ctc-1.1b.q8_0.gguf \
  --local-dir models

nemo-speech transcribe audio.wav \
  --model models/parakeet-ctc-1.1b.q8_0.gguf

See the NeMo-Speech.cpp documentation for more details.

NVIDIA NeMo

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 🤗.

Automatically instantiate the model

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="nvidia/parakeet-ctc-1.1b")

Transcribing using NeMo

First, let's get a sample

wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav

Then simply do:

asr_model.transcribe(['2086-149220-0033.wav'])

Transcribing using Transformers 🤗

Make sure to install transformers from source.

pip install git+https://github.com/huggingface/transformers

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-ctc-1.1b")
out = pipe("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")
print(out)

from transformers import AutoModelForCTC, AutoProcessor
from datasets import load_dataset, Audio
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-1.1b", dtype="auto", device_map=device)

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el['array'] for el in ds["audio"][:5]]

inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate)
inputs.to(model.device, dtype=model.dtype)
outputs = model.generate(**inputs)
print(processor.batch_decode(outputs))

from transformers import AutoModelForCTC, AutoProcessor
from datasets import load_dataset, Audio
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-1.1b", dtype="auto", device_map=device)

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
speech_samples = [el['array'] for el in ds["audio"][:5]]
text_samples = [el for el in ds["text"][:5]]

# passing `text` to the processor will prepare inputs' `labels` key
inputs = processor(audio=speech_samples, text=text_samples, sampling_rate=processor.feature_extractor.sampling_rate)
inputs.to(device, dtype=model.dtype)

outputs = model(**inputs)
outputs.loss.backward()

For more details about usage, the refer to Transformers' documentation.

Transcribing many audio files

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
 pretrained_name="nvidia/parakeet-ctc-1.1b"
 audio_dir=""

Input

This model accepts 16000 Hz mono-channel audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Model Architecture

FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. The model is trained using CTC loss. You may find more information on the details of FastConformer here: Fast-Conformer Model.

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.

The training dataset consists of private subset with 40K hours of Eng