| ---
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| language:
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| - ur
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| - sd
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| license: apache-2.0
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| pretty_name: Canopy-M
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| pipeline_tag: automatic-speech-recognition
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| library_name: pytorch
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| tags:
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| - automatic-speech-recognition
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| - speech
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| - ctc
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| - conformer
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| - urdu
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| - sindhi
|
| ---
|
|
|
| # Canopy-M
|
|
|
| <div align="center">
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| <img src="figures/canopy-m-logo.png" alt="Canopy-M Logo" width="220"/>
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| </div>
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|
|
| 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:
|
|
|
| ```bash
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| pip install torch torchaudio soundfile
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| pip install git+https://github.com/Proxima-AI-Co/canopy.git
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| hf auth login
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| canopy-transcribe clip.wav --model canopy-m --language urd
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| canopy-transcribe clip.wav --model ProximaAI/Canopy-M --language snd --device cpu
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| ```
|
|
|
| ```python
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| from canopy import Canopy
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|
|
| asr = Canopy.from_pretrained("canopy-m")
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| print(asr.transcribe("clip.wav", language="urd"))
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| print(asr.transcribe("clip.wav", language="snd"))
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| ```
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|
|
|
|
| ## 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` |
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| | Sindhi | FLEURS `sd_in` | 12.3 | 1.33 | human, CC-BY-4.0 |
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|
|
|
|
| ## 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.
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| - Common Voice Sindhi training audio is mostly unvalidated `other`, included with `--include-unvalidated`.
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| - Urdu Common Voice CER is 0.248. The headline 0.132 is pulled down by UrduSpeech and mahwiz.
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| - A wrong language id can cross Urdu and Sindhi, which share a Perso-Arabic script.
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| - No word n-gram was used in this table. Fusion can still overwrite a correct acoustic hypothesis.
|
|
|
| ## Citation
|
|
|
| ```bibtex
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| @software{canopy_m_2026,
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| title = {Canopy-M: Conformer-CTC speech recognition for Urdu and Sindhi},
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| author = {{Proxima AI}},
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| year = {2026},
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| url = {https://huggingface.co/ProximaAI/Canopy-M},
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| license = {Apache-2.0}
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| }
|
| ```
|
|
|
| Also cite Common Voice, and FLEURS.
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|
|
