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Model Overview

parakeet-tdt_ctc-110m is an ASR model that transcribes speech with Punctuations and Capitalizations of the English alphabet. This model is jointly developed by NVIDIA NeMo and Suno.ai teams. It is a Large version of Hybrid FastConformer [1] TDT-CTC [2] (around 114M parameters) model. This model has been trained with fastconformer architecture with full attention hence this model can transcribe up to 20 minutes of audio in one single pass. This model achieves super fast RTFx values across all datasets, with an average RTFx of ~4000 on Hugging Face leaderboard evaluation sets. 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']\n

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

import nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=\"parakeet-tdt_ctc-110m\")\n

Transcribing using Python

First, let's get a sample

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

Then simply do:

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

Transcribing many audio files

By default model uses TDT to transcribe the audio files, to switch decoder to use CTC, use decoding_type='ctc'

"\n\">

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \n pretrained_name=\"parakeet-tdt_ctc-110m\" \n audio_dir=\"<DIRECTORY CONTAINING AUDIO FILES>\"\n

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 Hybrid FastConformer-TDT-CTC 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.

Training

The NeMo toolkit [3] was used for finetuning this model for 20,000 steps over parakeet-tdt-1.1 model. This model is 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 36K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.

The training dataset consists of private subset with 27K hours of English speech plus 9k hours from the following public PnC datasets:

Librispeech 960 hours of English speech
Fisher Corpus
National Speech Corpus Part 1
VCTK
VoxPopuli (EN)
Europarl-ASR (EN)
Multilingual Librispeech (MLS EN) - 2,000 hour subset
Mozilla Common Voice (v7.0)

Performance

The performance of Automatic Speech Recognition models is measured 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 table 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.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Version	Tokenizer	Vocabulary Size	AMI	Earnings-22	Giga Speech	LS test-clean	LS test-other	SPGI Speech	TEDLIUM-v3	Vox Populi
2.0	BPE	1024	15.88	12.42	10.52	2.4	5.2	2.54	4.16	6.91

These are greedy WER numbers without external LM. More details on evaluation can be found at HuggingFace ASR Leaderboard

NVIDIA Riva: Deployment

NVIDIA Riva, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded.\nAdditionally, 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.
\nCheck out Riva live demo.

References

[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition

[2] Efficient Sequence Transduction by Jointly Predicting Tokens and Durations

[3] Google Sentencepiece Tokenizer

[4] NVIDIA NeMo Toolkit

[5] Suno.ai

[6] HuggingFace ASR Leaderboard

[7] Towards Measuring Fairness in AI: the Casual Conversations Dataset

Licence

License to use this model is covered by the CC-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 license.

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This model is jointly developed by NVIDIA NeMo and Suno.ai teams.","## Model Overview\n`parakeet-tdt_ctc-110m` is an ASR model that transcribes speech with Punctuations and Capitalizations of the 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 a Large version of Hybrid FastConformer [1] TDT-CTC [2] (around 114M parameters) model. This model has been trained with fastconformer architecture with full attention hence this model can transcribe up to 20 minutes of audio in one single pass. This model achieves super fast RTFx values across all datasets, with an average RTFx of ~4000 on Hugging Face leaderboard evaluation sets. 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.\n\n## NVIDIA NeMo: Training\n\nTo 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.\n```\npip install nemo_toolkit['all']\n``` \n\n## How to Use this Model\n\nThe 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.\n\n### Automatically instantiate the model\n\n```python\nimport nemo.collections.asr as nemo_asr\nasr_model = nemo_asr.models.ASRModel.from_pretrained(model_name=\"parakeet-tdt_ctc-110m\")\n```\n\n### Transcribing using Python\nFirst, let's get a sample\n```\nwget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav\n```\nThen simply do:\n```\nasr_model.transcribe(['2086-149220-0033.wav'])\n```\n\n### Transcribing many audio files\n\nBy default model uses TDT to transcribe the audio files, to switch decoder to use CTC, use decoding_type='ctc'\n\n```shell\npython [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \n pretrained_name=\"parakeet-tdt_ctc-110m\" \n audio_dir=\"\"\n```\n\n### Input\n\nThis model accepts 16000 Hz mono-channel audio (wav files) as input.\n\n### Output\n\nThis model provides transcribed speech as a string for a given audio sample.\n\n## Model Architecture\n\nThis model uses a Hybrid FastConformer-TDT-CTC 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).\n\n\n## Training\n\nThe NeMo toolkit [3] was used for finetuning this model for 20,000 steps over `parakeet-tdt-1.1` model. This model is trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/hybrid_transducer_ctc/fastconformer_hybrid_transducer_ctc_bpe.yaml).\n\nThe 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).\n\n### Datasets\n\nThe model was trained on 36K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.\n\nThe training dataset consists of private subset with 27K hours of English speech plus 9k hours from the following public PnC datasets:\n\n- Librispeech 960 hours of English speech\n- Fisher Corpus\n- National Speech Corpus Part 1\n- VCTK\n- VoxPopuli (EN)\n- Europarl-ASR (EN)\n- Multilingual Librispeech (MLS EN) - 2,000 hour subset\n- Mozilla Common Voice (v7.0)\n\n## Performance\n\nThe performance of Automatic Speech Recognition models is measured 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.\n\nThe following table 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. \n\n|**Version**|**Tokenizer**|**Vocabulary Size**|**AMI**|**Earnings-22**|**Giga Speech**|**LS test-clean**| **LS test-other** | **SPGI Speech**|**TEDLIUM-v3**|**Vox Populi**|\n|---------|-----------------------|-----------------|---------|---------------|------------|-----------|---------------|-------------|------|------|\n| 2.0 | BPE | 1024 | 15.88 | 12.42 | 10.52 | 2.4 | 5.2 | 2.54 | 4.16 | 6.91 | \n\nThese 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)\n\n\n## NVIDIA Riva: Deployment\n\n[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. \nAdditionally, Riva provides: \n\n* 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 \n* 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 \n* Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support \n\nAlthough this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva). \nCheck out [Riva live demo](https://developer.nvidia.com/riva#demos). \n\n## References\n[1] [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://arxiv.org/abs/2305.05084)\n\n[2] [Efficient Sequence Transduction by Jointly Predicting Tokens and Durations](https://arxiv.org/abs/2304.06795)\n\n[3] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)\n\n[4] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n[5] [Suno.ai](https://suno.ai/)\n\n[6] [HuggingFace ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)\n\n[7] [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://arxiv.org/abs/2104.02821)\n\n\n## Licence\n\nLicense 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.","displayName","Parakeet-TDT_CTC-110M",["D",1732745356780],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/parakeet-tdt_ctc-110m","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n|--------------------------------------------------------------------------------------------------------|----------|\n| If any special measures have been taken to mitigate against unwanted bias? | No |\n| Was model evaluated for bias? | No |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | No |","explainability","| Field | Response |\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| High-level application domain: | ASR |\n| Describe the model input and output, e.g. input: streaming speech / file; output: Text with / without capitalization and punctuation. | This model accepts 16000 Hz Mono-channel Audio (wav files) as input and provides transcribed speech as a string for a given audio sample as output. |\n| Is this streaming / offline model? | Offline |\n| Explain model architecture e.g., encoder-decoder with RNNT loss / Tokenizer … | FastConformer is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. The model is trained in a multitask setup with joint TDT and CTC decoder loss. |\n| Number of parameters | 114M |\n| List the technical limitations of the model. | The model is non-streaming and outputs the speech as a string without capitalization and punctuation. Since this model was trained on publicly available speech datasets, performance might degrade for speech with untrained terms.|","privacy","| Field | Response |\n|--------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------|\n| Generatable or reverse engineerable personal data? | None |\n| If applicable, was a notice provided to the individuals prior to the collection of any personal data used? | Not Applicable |\n| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\n| Is there dataset provenance? (Do you know where the data came from?) | Yes |\n| If data is labeled, was it reviewed by legal on compliance privacy laws? Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |\n| Is a mechanism in place to honor data subject rights of access or deletion of personal data? | No |","safetyAndSecurity","| Field | Response |\n|--------------------------------------------------------------------------------------------------------|----------|\n| End user license agreement.? | CC-BY-4.0 |\n| Potential model risks | The model's performance may be sensitive to noise, and it may occasionally produce repeated words or omit some words in the expected transcript. 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