Canopy-M

Canopy-M is a 57.9-million-parameter Conformer-CTC model for Urdu and Sindhi. It is non-autoregressive: a Macaron encoder predicts a token at every frame, and greedy CTC reads that sequence.

Use

Install the library once, then point it at any published variant:

pip install torch torchaudio soundfile
pip install git+https://github.com/Proxima-AI-Co/canopy.git
hf auth login
canopy-transcribe clip.wav --model canopy-m --language urd
canopy-transcribe clip.wav --model ProximaAI/Canopy-M --language snd --device cpu

from canopy import Canopy

asr = Canopy.from_pretrained("canopy-m")
print(asr.transcribe("clip.wav", language="urd"))
print(asr.transcribe("clip.wav", language="snd"))

Training data

About 186.7 hours were used in training.

Language	Source	Train hours	Dev hours	Labels
Urdu	Common Voice + mahwiz + UrduSpeech	~79.6	~17	human
Sindhi	Common Voice	71.5	0.10	human; mostly unvalidated other
Sindhi	FLEURS sd_in	12.3	1.33	human, CC-BY-4.0

Intended use

Research transcription of read Urdu and Sindhi at 16 kHz, one language per utterance, with the language given.

Limitations

• Sindhi Common Voice development audio is 0.10 hours (70 utterances). Most of the Sindhi score is FLEURS.
• Common Voice Sindhi training audio is mostly unvalidated other, included with --include-unvalidated.
• Urdu Common Voice CER is 0.248. The headline 0.132 is pulled down by UrduSpeech and mahwiz.
• A wrong language id can cross Urdu and Sindhi, which share a Perso-Arabic script.
• No word n-gram was used in this table. Fusion can still overwrite a correct acoustic hypothesis.

Citation

@software{canopy_m_2026,
  title = {Canopy-M: Conformer-CTC speech recognition for Urdu and Sindhi},
  author = {{Proxima AI}},
  year = {2026},
  url = {https://huggingface.co/ProximaAI/Canopy-M},
  license = {Apache-2.0}
}

Also cite Common Voice, and FLEURS.