Update README.md

README.md

@@ -7,4 +7,48 @@ language:
 base_model:
 - dvislobokov/whisper-large-v3-turbo-russian
 pipeline_tag: automatic-speech-recognition
----
+---
+
+## Example of use this model with faster-whisper
+
+```python
+import io
+import json
+import logging
+import sys
+import time
+from datetime import datetime
+from faster_whisper import WhisperModel
+from pydub import AudioSegment
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler('faster-whisper.log'),
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+model = WhisperModel("/path/to/dvislobokov/faster-whisper-large-v3-turbo-russian", "cpu")
+
+audio = AudioSegment.from_wav("ezyZip.wav")
+chunk_length = 30 * 1000 # in milliseconds
+chunks = [audio[i:i + chunk_length] for i in range(0, len(audio), chunk_length)]
+
+
+logging.info(f'Start transcribe at {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')
+start = time.time()
+
+text = []
+for i, chunk in enumerate(chunks):
+    buffer = io.BytesIO()
+    chunk.export(buffer, format="wav")
+    segments, info = model.transcribe(buffer, language="ru")
+    text.append("".join(segment.text for segment in segments))
+end = time.time()
+logging.info(f'Finish transcribe at {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')
+logging.info(f'Total time: {end - start}')
+logging.info(f'Text: {text}')
+```
+
+