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MohammadGholizadeh
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Model Details
Model Provider
MohammadGholizadeh
Model Tree
openai/whisper-large-v3-turbo
Input Modalities
Output Modalities
Supported Functionality
Greedy decoding with Persian forced. WER and CER are computed after normalizing both reference and hypothesis: ي/ی, ك/ک, ZWNJ, diacritics, punctuation and tatweel are removed. "CER (no spaces)" also ignores spacing. Persian often writes the same word with or without a space (میشیم / می شیم), and plain WER counts those variants as errors.
| Test set | Clips | WER base → fine-tuned | CER (no spaces) base → fine-tuned |
|---|---|---|---|
| Common Voice 17 fa test (speakers unseen in training) | 10,558 | 50.2% → 19.4% | 23.6% → 4.2% |
| YouTube, 82 videos fully held out | 1,326 | 45.3% → 20.2% | 24.4% → 10.0% |
| Filimo test (5k sample, ~2 s movie dialogue) | 5,000 | 86.5% → 31.2% | 50.4% → 16.8% |
python
import torchfrom transformers import pipeline asr = pipeline("automatic-speech-recognition", model="MohammadGholizadeh/whisper-large-v3-turbo-persian", dtype=torch.bfloat16, device="cuda")gen = {"language": "fa", "task": "transcribe"} print(asr("clip.wav", generate_kwargs=gen)["text"]) # file input needs ffmpeg installed import soundfile as sf # or pass audio yourself, no ffmpeg neededaudio, sr = sf.read("clip.wav", dtype="float32")print(asr({"raw": audio, "sampling_rate": sr}, generate_kwargs=gen)["text"])Audio (mono) is resampled to 16 kHz automatically. The model was trained on clips of 0.5–20 s. For longer recordings pass chunk_length_s=30 to the pipeline.
Code: https://github.com/msghik/embeddinggemma2-speech-retrieval
import torchfrom transformers import pipeline asr = pipeline("automatic-speech-recognition", model="MohammadGholizadeh/whisper-large-v3-turbo-persian", dtype=torch.bfloat16, device="cuda")gen = {"language": "fa", "task": "transcribe"} print(asr("clip.wav", generate_kwargs=gen)["text"]) # file input needs ffmpeg installed import soundfile as sf # or pass audio yourself, no ffmpeg neededaudio, sr = sf.read("clip.wav", dtype="float32")print(asr({"raw": audio, "sampling_rate": sr}, generate_kwargs=gen)["text"])