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All models by nvidia →🎉 NEW: Canary 1B V2 is now available!
🌍 25 European Languages | ⏱️ Much Improved Timestamp Prediction | 🔄 Enhanced ASR & AST | 🔗 Try it here: nvidia/canary-1b-v2
NVIDIA NeMo Canary Flash [1] is a family of multilingual multi-tasking models based on Canary architecture [2] that achieve state-of-the-art performance on multiple speech benchmarks. With 883 million parameters and an inference speed of more than 1000 RTFx (on open-asr-leaderboard datasets), canary-1b-flash supports automatic speech-to-text recognition (ASR) in four languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC). Additionally, canary-1b-flash offers an experimental feature for word-level and segment-level timestamps in English, German, French, and Spanish. This model is released under the permissive CC-BY-4.0 license and is available for commercial use.
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What is Nemotron?
NVIDIA Developer Nemotron
NVIDIA Riva Speech
NeMo Documentation
Canary is an encoder-decoder model with FastConformer [3] Encoder and Transformer Decoder [4]. With audio features extracted from the encoder, task tokens such as <target language>, <task>, <toggle timestamps> and <toggle PnC> are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer [5] from individual SentencePiece [6] tokenizers of each language, which makes it easy to scale up to more languages. The canary-1b-flash model has 32 encoder layers and 4 decoder layers, leading to a total of 883M parameters. For more details about the architecture, please refer to [1].
To train, fine-tune or transcribe with canary-1b-flash, you will need to install NVIDIA NeMo.
The model is available for use in the NeMo Framework [7], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
Please refer to our tutorial for more details.
A few inference examples are listed below:
Input Type(s): Audio
Input Format(s): .wav or .flac files
Input Parameters(s): 1D
Other Properties Related to Input: 16000 Hz Mono-channel Audio, Pre-Processing Not Needed
Input to canary-1b-flash can be either a list of paths to audio files or a jsonl manifest file.
If the input is a list of paths, canary-1b-flash assumes that the audio is English and transcribes it. I.e., canary-1b-flash default behavior is English ASR.
canary-1b-flash can also generate word and segment level timestamps
For audio files longer than 10 seconds, we recommend using longform inference script (explained in next section) with chunk_len_in_secs=10.0 to generate timestamps.
To use canary-1b-flash for transcribing other supported languages or perform Speech-to-Text translation or provide word-level timestamps, specify the input as jsonl manifest file, where each line in the file is a dictionary containing the following fields:
and then use:
Canary models are designed to handle input audio smaller than 40 seconds. In order to handle longer audios, NeMo includes speech_to_text_aed_chunked_infer.py script that handles chunking, performs inference on the chunked files, and stitches the transcripts.
The script will perform inference on all .wav files in audio_dir. Alternatively you can also pass a path to a manifest file as shown above. The decoded output will be saved at output_json_path.
Note that for longform inference with timestamps, it is recommended to use chunk_len_in_secs of 10 seconds.
Output Type(s): Text
Output Format: Text output as a string (w/ timestamps) depending on the task chosen for decoding
Output Parameters: 1-Dimensional text string
Other Properties Related to Output: May Need Inverse Text Normalization; Does Not Handle Special Characters
Runtime Engine(s):
Supported Hardware Microarchitecture Compatibility:
[Preferred/Supported] Operating System(s):
canary-1b-flash
The canary-1b-flash model is trained on a total of 85K hrs of speech data. It consists of 31K hrs of public data, 20K hrs collected by Suno, and 34K hrs of in-house data. The datasets below include conversations, videos from the web and audiobook recordings.
Data Collection Method:
Labeling Method:
The constituents of public data are as follows.
Data Collection Method:
Labeling Method:
Automatic Speech Recognition:
Automatic Speech Translation:
Timestamp Prediction:
Hallucination Robustness:
Noise Robustness:
Model Fairness:
Canary-1B-Flash is trained using the NVIDIA NeMo Framework [7] for a total of 200K steps with 2D bucketing [1] and optimal batch sizes set using OOMptimizer [8].The model is trained on 128 NVIDIA A100 80GB GPUs. The model can be trained using this example script and base config.
The tokenizers for these models were built using the text transcripts of the train set with this script.
Engine: NVIDIA NeMo
Test Hardware :
For ASR and AST experiments, predictions were generated using greedy decoding. Note that utterances shorter than 1 second are symmetrically zero-padded upto 1 second during evaluation.
The ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with whisper-normalizer.
WER on HuggingFace OpenASR leaderboard:
We profiled inference speed on the OpenASR benchmark (batch_size=128) using the real-time factor (RTFx) to quantify throughput.
WER on MLS test set:
WER on MCV-16.1 test set:
More details on evaluation can be found at HuggingFace ASR Leaderboard
We evaluate AST performance with BLEU score and COMET score, and use native annotations with punctuation and capitalization in the datasets.
FLEURS test set:
BLEU score:
COMET score:
COVOST-v2 test set:
BLEU score:
COMET score:
mExpresso test set:
BLEU score:
COMET score:
F1-score on Librispeech Test sets at collar value of 200ms
Number of characters per minute on MUSAN 48 hrs eval set
WER on Librispeech Test Clean at different SNR (signal to noise ratio) levels of additive white noise
As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset" [9], we assessed the canary-1b-flash model for fairness. The model was evaluated on the CausalConversations-v1 dataset, and the results are reported as follows:
(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.)
[1] Training and Inference Efficiency of Encoder-Decoder Speech Models
[2] Less is More: Accurate Speech Recognition & Translation without Web-Scale Data
[3] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
[6] Google Sentencepiece Tokenizer
[8] EMMeTT: Efficient Multimodal Machine Translation Training
[9] Towards Measuring Fairness in AI: the Casual Conversations Dataset
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from nemo.collections.asr.models import EncDecMultiTaskModel# load modelcanary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b-flash')# update decode paramsdecode_cfg = canary_model.cfg.decodingdecode_cfg.beam.beam_size = 1canary_model.change_decoding_strategy(decode_cfg)output = canary_model.transcribe( ['path1.wav', 'path2.wav'], batch_size=16, # batch size to run the inference with pnc='yes', # generate output with Punctuation and Capitalization)
predicted_text_1 = output[0].textoutput = canary_model.transcribe( ['filepath.wav'], timestamps='yes', # generate output with timestamps)
predicted_text = output[0].textword_level_timestamps = output[0].timestamp['word']segment_level_timestamps = output[0].timestamp['segment']# Example of a line in input_manifest.json{ "audio_filepath": "/path/to/audio.wav", # path to the audio file "source_lang": "en", # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr'] "target_lang": "en", # language of the text output, choices=['en','de','es','fr'] "pnc": "yes", # whether to have PnC output, choices=['yes', 'no'] "timestamp": "yes", # whether to output word-level timestamps, choices=['yes', 'no']}output = canary_model.transcribe( "<path to input manifest file>", batch_size=16, # batch size to run the inference with)python scripts/speech_to_text_aed_chunked_infer.py \ pretrained_name="nvidia/canary-1b-flash" \ audio_dir=$audio_dir \ output_filename=$output_json_path \ chunk_len_in_secs=40.0 \ batch_size=1 \ decoding.beam.beam_size=1 \ timestamps=False