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	---
	language:
	- en
	library_name: nemo
	datasets:
	- librispeech_asr
	- fisher_corpus
	- Switchboard-1
	- WSJ-0
	- WSJ-1
	- National-Singapore-Corpus-Part-1
	- National-Singapore-Corpus-Part-6
	- vctk
	- voxpopuli
	- europarl
	- multilingual_librispeech
	- mozilla-foundation/common_voice_8_0
	- MLCommons/peoples_speech
	thumbnail: null
	tags:
	- automatic-speech-recognition
	- speech
	- audio
	- Transducer
	- TDT
	- FastConformer
	- Conformer
	- pytorch
	- NeMo
	- hf-asr-leaderboard
	license: cc-by-4.0
	widget:
	- example_title: Librispeech sample 1
	src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
	- example_title: Librispeech sample 2
	src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
	model-index:
	- name: parakeet_tdt_1.1b
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: AMI (Meetings test)
	type: edinburghcstr/ami
	config: ihm
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 15.90
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Earnings-22
	type: revdotcom/earnings22
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 14.65
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: GigaSpeech
	type: speechcolab/gigaspeech
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 9.55
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: LibriSpeech (clean)
	type: librispeech_asr
	config: other
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 1.39
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: LibriSpeech (other)
	type: librispeech_asr
	config: other
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 2.62
	- task:
	type: Automatic Speech Recognition
	name: automatic-speech-recognition
	dataset:
	name: SPGI Speech
	type: kensho/spgispeech
	config: test
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 3.42
	- task:
	type: Automatic Speech Recognition
	name: automatic-speech-recognition
	dataset:
	name: tedlium-v3
	type: LIUM/tedlium
	config: release1
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 3.56
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: Vox Populi
	type: facebook/voxpopuli
	config: en
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 5.48
	- task:
	type: Automatic Speech Recognition
	name: automatic-speech-recognition
	dataset:
	name: Mozilla Common Voice 9.0
	type: mozilla-foundation/common_voice_9_0
	config: en
	split: test
	args:
	language: en
	metrics:
	- name: Test WER
	type: wer
	value: 5.97

	metrics:
	- wer
	pipeline_tag: automatic-speech-recognition
	---

	# Parakeet TDT 1.1B (en)

	<style>
	img {
	display: inline;
	}
	</style>

	[![Model architecture](https://img.shields.io/badge/Model_Arch-FastConformer--TDT-lightgrey#model-badge)](#model-architecture)
	\| [![Model size](https://img.shields.io/badge/Params-1.1B-lightgrey#model-badge)](#model-architecture)
	\| [![Language](https://img.shields.io/badge/Language-en-lightgrey#model-badge)](#datasets)


	`parakeet-tdt-1.1b` is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) and [Suno.ai](https://www.suno.ai/) teams.
	It is an XXL version of FastConformer [1] TDT [2] (around 1.1B parameters) model.
	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.

	## NVIDIA NeMo: Training

	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.
	```
	pip install nemo_toolkit['all']
	```

	## How to Use this Model

	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.

	### Automatically instantiate the model

	```python
	import nemo.collections.asr as nemo_asr
	asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="nvidia/parakeet-tdt-1.1b")
	```

	### Transcribing using Python
	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 many audio files

	```shell
	python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
	pretrained_name="nvidia/parakeet-tdt-1.1b"
	audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
	```

	### 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

	This model uses a FastConformer-TDT architecture. FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer).

	TDT (Token-and-Duration Transducer) [2] is a generalization of conventional Transducers by decoupling token and duration predictions. Unlike conventional Transducers which produces a lot of blanks during inference, a TDT model can skip majority of blank predictions by using the duration output (up to 4 frames for this parakeet-tdt-1.1b model), thus brings significant inference speed-up. The detail of TDT can be found here: [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795).

	## Training

	The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml).

	The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).

	### 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 English speech plus 24K hours from the following public datasets:

	- Librispeech 960 hours of English speech
	- Fisher Corpus
	- Switchboard-1 Dataset
	- WSJ-0 and WSJ-1
	- National Speech Corpus (Part 1, Part 6)
	- VCTK
	- VoxPopuli (EN)
	- Europarl-ASR (EN)
	- Multilingual Librispeech (MLS EN) - 2,000 hour subset
	- Mozilla Common Voice (v7.0)
	- People's Speech - 12,000 hour subset

	## Performance

	The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.

	The following tables summarizes the performance of the available models in this collection with the Transducer decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.

	\|Version\|Tokenizer\|Vocabulary Size\|AMI\|Earnings-22\|Giga Speech\|LS test-clean\|SPGI Speech\|TEDLIUM-v3\|Vox Populi\|Common Voice\|
	\|---------\|-----------------------\|-----------------\|---------------\|---------------\|------------\|-----------\|-----\|-------\|------\|------\|
	\| 1.22.0 \| SentencePiece Unigram \| 1024 \| 15.90 \| 14.65 \| 9.55 \| 1.39 \| 2.62 \| 3.42 \| 3.56 \| 5.48 \| 5.97 \|

	These are greedy WER numbers without external LM. More details on evaluation can be found at [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)


	## Model Fairness Evaluation

	As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset", we assessed the parakeet-tdt-1.1b model for fairness. The model was evaluated on the CausalConversations-v1 dataset, and the results are reported as follows:

	### Gender Bias:

	\| Gender \| Male \| Female \| N/A \| Other \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| Num utterances \| 19325 \| 24532 \| 926 \| 33 \|
	\| % WER \| 17.18 \| 14.61 \| 19.06 \| 37.57 \|

	### Age Bias:

	\| Age Group \| $(18-30)$ \| $(31-45)$ \| $(46-85)$ \| $(1-100)$ \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| Num utterances \| 15956 \| 14585 \| 13349 \| 43890 \|
	\| % WER \| 15.83 \| 15.89 \| 15.46 \| 15.74 \|

	(Error rates for fairness evaluation are determined by normalizing both the reference and predicted text, similar to the methods used in the evaluations found at https://github.com/huggingface/open_asr_leaderboard.)

	## NVIDIA Riva: Deployment

	[NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.
	Additionally, Riva provides:

	* World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
	* Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
	* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support

	Although this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva).
	Check out [Riva live demo](https://developer.nvidia.com/riva#demos).

	## References
	[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)

	[2] [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795)

	[3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)

	[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

	[5] [Suno.ai](https://suno.ai/)

	[6] [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)

	[7] [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://arxiv.org/abs/2104.02821)


	## Licence

	License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.