2e8c2c11d0
Vocabulary study with FSRS spaced repetition, AI tutoring (Ollama/Claude), essay marking, idioms browser, Anki export, and dashboard. 918 vocabulary entries across 39 categories. 41 tests passing. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
"""Persian speech-to-text wrapper using sttlib."""
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import sys
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import numpy as np
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sys.path.insert(0, "/home/ys/family-repo/Code/python/tool-speechtotext")
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from sttlib import load_whisper_model, transcribe, is_hallucination
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_model = None
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# Common Whisper hallucinations in Persian/silence
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PERSIAN_HALLUCINATIONS = [
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"ممنون", # "thank you" hallucination
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"خداحافظ", # "goodbye" hallucination
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"تماشا کنید", # "watch" hallucination
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"لایک کنید", # "like" hallucination
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]
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def get_model(size="medium"):
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"""Load Whisper model (cached singleton)."""
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global _model
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if _model is None:
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_model = load_whisper_model(size)
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return _model
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def transcribe_persian(audio_tuple):
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"""Transcribe Persian audio from Gradio audio component.
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Args:
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audio_tuple: (sample_rate, numpy_array) from gr.Audio component.
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Returns:
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Transcribed text string, or empty string on failure/hallucination.
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"""
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if audio_tuple is None:
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return ""
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sr, audio = audio_tuple
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model = get_model()
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# Convert to float32 normalized [-1, 1]
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if audio.dtype == np.int16:
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audio_float = audio.astype(np.float32) / 32768.0
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elif audio.dtype == np.float32:
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audio_float = audio
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else:
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audio_float = audio.astype(np.float32) / np.iinfo(audio.dtype).max
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# Mono conversion if stereo
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if audio_float.ndim > 1:
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audio_float = audio_float.mean(axis=1)
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# Use sttlib transcribe
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text = transcribe(model, audio_float)
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# Filter hallucinations (English + Persian)
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if is_hallucination(text):
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return ""
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if text.strip() in PERSIAN_HALLUCINATIONS:
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return ""
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return text
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