Cohere Transcribe Arabic
About this model
Cohere Transcribe Arabic is a finetuned version of Cohere Transcribe that is optimized for Arabic audio inputs. Like Cohere Transcribe, it is a 2B parameters dedicated audio-in, text-out, automatic speech recognition (ASR) model available open source. The model is designed to accurately capture the diversity of dialects, accents, and acoustic conditions among Arabic speakers during transcription.
Model details
- Input: Audio waveform
- Output: Text
- Model name:
cohere-transcribe-arabic-07-2026 - Languages covered: Arabic, English
- Multidialectal support: Yes
- Code-switching support: Yes
- Maximum file size: 25MB
- License: Apache 2.0
Availability
You can access Cohere Transcribe Arabic via our API for free, low-setup experimentation subject to rate limits.
For production deployment without rate limits, provision a dedicated Model Vault. This enables low-latency, private cloud inference without having to manage infrastructure. Pricing is calculated per hour-instance, with discounted plans for longer-term commitments. Contact our team to discuss your requirements.
Strengths
Cohere Transcribe Arabic achieves state-of-the-art transcription accuracy for Arabic speech. These results are robust to diverse or variable audio inputs, including: bilingual dialogues (Arabic-English); regional dialects and phrasing, and enterprise-specific vocabulary. Like its parent model, Cohere Transcribe Arabic has been optimized for high-throughput, production inference, and is at the frontier for far-field (where the speaker is not close to the receiver) transcription tasks.
Key Limitations
- Timestamps: The model does not output timestamps alongside transcripts.
- Speaker diarization: The model does not automatically identify individual speakers in a multispeaker audio file.
Model architecture
Cohere Transcribe is built on a speech-optimized Transformer variant: a Conformer. Input audio waveforms are converted into a Mel spectrogram and then processed by a Conformer encoder that holds the majority of the model’s parameters. The encoder’s representations are then passed to a lightweight Transformer decoder that generates text tokens. Cohere Transcribe is trained using standard supervised cross-entropy.