Ported from upstream commit
b12ab41,
pinned 2026-05-08.
Validated against the NeMo reference at transcribe.cpp commit
db53eda
on 2026-05-08.
Offline multilingual speech-to-text and translation. A 182M-parameter
multitask AED with a 17-layer FastConformer encoder and a 4-layer
Transformer decoder. Supports automatic speech recognition in English,
German, Spanish, and French, and bidirectional EN↔{DE, ES, FR}
translation. Takes a
16 kHz mono WAV and produces a transcript. Not a streaming model;
word/segment timestamps are upstream-experimental and not exposed in
the v1 port.
WER measured on the full LibriSpeech test-clean split (2620 utterances)
with greedy decoding and no external LM. F32 reference baseline: 1.94%.
On the same wavs, NeMo's reference run produces 1.93% (one substitution
difference out of ~27k reference words), so the F32 port matches the
reference framework at the noise floor. NVIDIA's self-reported number
on the upstream model card is 1.87%.
If your audio isn't already 16 kHz mono WAV, convert it first:
ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
See the transcribe.cpp model page for performance
numbers, numerical validation, and reproduction steps.
License
Inherited from the base model: CC-BY-4.0. See the
upstream model card for full terms.
Original Model Card
The section below is reproduced from
nvidia/canary-180m-flash at commit
b12ab41 for offline reference. The upstream card is the
authoritative source.
Canary 180M Flash
Description:
NVIDIA NeMo Canary Flash [1] is a family of multilingual multi-tasking models based on Canary architecture [2] that achieves state-of-the art performance on multiple speech benchmarks. With 182 million parameters and an inference speed of more than 1200 RTFx (on open-asr-leaderboard sets), canary-180m-flash supports automatic speech-to-text recognition (ASR) in 4 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-180m-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.
Model Architecture:
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-180m-flash model has 17 encoder layers and 4 decoder layers, leading to a total of 182M parameters. For more details about the architecture, please refer to [1].
NVIDIA NeMo
To train, fine-tune or transcribe with canary-180m-flash, you will need to install NVIDIA NeMo.
How to Use this Model
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.
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-180m-flash can be either a list of paths to audio files or a jsonl manifest file.
Inference with canary-180m-flash:
If the input is a list of paths, canary-180m-flash assumes that the audio is English and transcribes it. I.e., canary-180m-flash default behavior is English ASR.
python
1output = canary_model.transcribe(2['path1.wav','path2.wav'],3 batch_size=16,# batch size to run the inference with4 pnc='True',# generate output with Punctuation and Capitalization5)67predicted_text = output[0].text
8
canary-180m-flash can also predict word-level and segment-level timestamps
To predict timestamps for audio files longer than 10 seconds, we recommend using the longform inference script (explained in the next section) with chunk_len_in_secs=10.0.
To use canary-180m-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:
yaml
1# Example of a line in input_manifest.json2{3"audio_filepath":"/path/to/audio.wav",# path to the audio file4"source_lang":"en",# language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']5"target_lang":"en",# language of the text output, choices=['en','de','es','fr']6"pnc":"yes",# whether to have PnC output, choices=['yes', 'no']7"timestamp":"yes",# whether to output word-level timestamps, choices=['yes', 'no']8}
and then use:
python
1output = canary_model.transcribe(2"<path to input manifest file>",3 batch_size=16,# batch size to run the inference with4)
Longform inference with canary-180m-flash:
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:
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
The canary-180m-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.
canary-180m-flash is trained using the NVIDIA NeMo framework [7] for a total of 219K steps with 2D bucketing [1] and optimal batch sizes set using OOMptimizer [8]. The model is trained on 32 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.
Inference:
Engine: NVIDIA NeMo Test Hardware :
A6000
A100
V100
Performance
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.
English ASR Performance (w/o PnC)
The ASR performance is measured with word error rate (WER), and we process the groundtruth and predicted text with whisper-normalizer.
Number of characters per minute on MUSAN 48 hrs eval set
Version
Model
# of character per minute
main
canary-180m-flash
91.52
Noise Robustness
WER on Librispeech Test Clean at different SNR (signal to noise ratio) levels of additive white noise
Version
Model
SNR 10
SNR 5
SNR 0
SNR -5
main
canary-180m-flash
3.23
5.34
12.21
34.03
Model Fairness Evaluation
As outlined in the paper "Towards Measuring Fairness in AI: the Casual Conversations Dataset" [9], we assessed the canary-180m-flash 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
16.92
14.01
20.01
25.04
Age Bias:
Age Group
(18-30)
(31-45)
(46-85)
(1-100)
Num utterances
15956
14585
13349
43890
% WER
14.95
15.36
15.65
15.29
(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.)
License/Terms of Use:
canary-180m-flash is released under the CC-BY-4.0 license. By using this model, you are agreeing to the terms and conditions of the license.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report security vulnerabilities or NVIDIA AI Concerns here.
