Taraneh-WBase: Persian Automatic Speech Recognition
Taraneh-WBase is the Whisper-Base member of Taraneh, a family of Persian ASR models fine-tuned on about 1,560 hours of speech. The base checkpoint is openai/whisper-base.
License
MIT, the same license as OpenAI Whisper. Commercial use is allowed, including in products and hosted services. Keep this notice and the original Whisper MIT notice with the weights.
Paper
Aref Farhadipour. Taraneh-ASR: Scaling Persian Speech Recognition and Forced Alignment Across Singing and Conversational Domains. University of Zurich.
- PDF in the code repository: https://github.com/areffarhadi/persian-asr/blob/main/ArefFarhadi-TaranehASR.pdf
- Code, manifests, normalization, and inference: https://github.com/areffarhadi/persian-asr
Load this checkpoint with Transformers (WhisperForConditionalGeneration). CTranslate2 is the fast path in the GitHub scripts.
Results
Word error rate / character error rate (%) after the unified Persian text normalization. PSRB and VisualEars were unseen in training.
| FLEURS | Common Voice | YouTube | Music | Media | PSRB | VisualEars |
|---|---|---|---|---|---|---|
| 7.88 / 2.93 | 10.76 / 4.45 | 19.02 / 10.30 | 37.69 / 17.67 | 24.43 / 13.46 | 26.88 / 12.26 | 11.58 / 3.97 |
The full comparison of all five Taraneh models is in the paper and in the GitHub README.