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areffarhadi
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Model Details
Model Provider
areffarhadi
Model Tree
openai/whisper-large-v3
Input Modalities
Output Modalities
Supported Functionality
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.
Aref Farhadipour. Taraneh-ASR: Scaling Persian Speech Recognition and Forced Alignment Across Singing and Conversational Domains. University of Zurich.
Load this checkpoint with Transformers (WhisperForConditionalGeneration). CTranslate2 is the fast path in the GitHub scripts.
This checkpoint can return word times as well as a transcript. In the GitHub scripts, --task asr writes the transcript and --task asr+fa also writes alignments.jsonl.
The readout uses decoder cross-attention heads L23/H3 and L26/H7, a median filter of width 3, column normalization, dynamic time warping, and a −35 dB trim of the first-word start and last-word end. On the FarsDat gold set this model reaches 38.1 ms mean absolute error. Heads and the command are in the GitHub README.
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 |
|---|---|---|---|---|---|---|
| 3.86 / 1.33 | 3.34 / 1.53 | 13.42 / 7.99 | 22.98 / 11.00 | 16.06 / 9.54 | 18.00 / 8.17 | 5.31 / 1.56 |
The full comparison of all five Taraneh models is in the paper and in the GitHub README.