Canary-180m-flash
| Property | Value |
|---|---|
| Parameter Count | 182 Million |
| License | CC-BY-4.0 |
| Architecture | FastConformer Encoder + Transformer Decoder |
| Developer | NVIDIA |
| Training Data | 85,000 hours of multilingual speech |
What is canary-180m-flash?
Canary-180m-flash is NVIDIA's state-of-the-art multilingual speech model that achieves remarkable performance in automatic speech recognition (ASR) and translation. With 182 million parameters, it processes audio at over 1200 times real-time speed while supporting four languages: English, German, French, and Spanish. The model excels in both ASR and translation tasks, featuring innovative capabilities like word-level timestamps and punctuation prediction.
Implementation Details
The model utilizes a FastConformer encoder coupled with a Transformer decoder architecture, incorporating 17 encoder layers and 4 decoder layers. It employs a concatenated tokenizer built from individual SentencePiece tokenizers for each supported language, enabling efficient multilingual processing. The model operates on 16kHz mono-channel audio and can handle various input formats including .wav and .flac files.
- Trained on 85,000 hours of diverse speech data
- Supports bidirectional translation between English and German/French/Spanish
- Features automatic punctuation and capitalization
- Provides word-level and segment-level timestamp capabilities
Core Capabilities
- High-speed ASR with 1200+ RTFx on modern GPUs
- Multilingual speech recognition with state-of-the-art accuracy
- Speech-to-text translation across supported language pairs
- Timestamp generation for precise word alignment
- Support for long-form audio through chunked processing
Frequently Asked Questions
Q: What makes this model unique?
The model combines high performance with impressive speed, achieving state-of-the-art results while maintaining real-time factor exceeding 1200x. Its ability to handle multiple languages and tasks within a relatively compact 182M parameter architecture makes it particularly efficient and versatile.
Q: What are the recommended use cases?
The model is ideal for applications requiring fast, accurate speech transcription and translation, including media subtitling, content localization, and real-time speech processing. It's particularly suited for scenarios requiring timestamp information or handling multiple languages within the supported set.
