pierreguillou
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48dfdd1
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Parent(s):
b6cd1d1
Upload audio_to_transcript.ipynb
Browse files- docs/audio_to_transcript.ipynb +1633 -0
docs/audio_to_transcript.ipynb
ADDED
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],
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],
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"\n",
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"model_size = \"large-v2\"\n",
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"\n",
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"\n",
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" model_whisper = WhisperModel(model_size, device=\"cuda\", compute_type=\"float16\")\n",
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" # or Run on GPU with INT8\n",
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" # model = WhisperModel(model_size, device=\"cuda\", compute_type=\"int8_float16\")\n",
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"else:\n",
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" # Run on CPU with INT8\n",
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{
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
|
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"def get_filename(file_obj):\n",
|
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" return file_obj.name.split(\"/\")[-1]\n",
|
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"\n",
|
1565 |
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"def audio_to_transcript(file_obj):\n",
|
1566 |
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" # get all audio segments\n",
|
1567 |
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" try:\n",
|
1568 |
+
" filename = get_filename(file_obj)\n",
|
1569 |
+
" segments, _ = model_whisper.transcribe(file_obj.name, beam_size=5, vad_filter=True)\n",
|
1570 |
+
" except:\n",
|
1571 |
+
" filename = file_obj.split(\"/\")[-1]\n",
|
1572 |
+
" segments, _ = model_whisper.transcribe(file_obj, beam_size=5, vad_filter=True)\n",
|
1573 |
+
"\n",
|
1574 |
+
" start_segments, end_segments, text_segments = list(), list(), list()\n",
|
1575 |
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" for segment in segments:\n",
|
1576 |
+
" start, end, text = segment.start, segment.end, segment.text\n",
|
1577 |
+
" start_segments.append(start)\n",
|
1578 |
+
" end_segments.append(end)\n",
|
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+
" text_segments.append(text)\n",
|
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+
"\n",
|
1581 |
+
" # save transcript into csv\n",
|
1582 |
+
" df = pd.DataFrame()\n",
|
1583 |
+
" df[\"start\"] = start_segments\n",
|
1584 |
+
" df[\"end\"] = end_segments\n",
|
1585 |
+
" df[\"text\"] = text_segments\n",
|
1586 |
+
"\n",
|
1587 |
+
" csv_file = filename.split(\".\")[0] + \".csv\"\n",
|
1588 |
+
" df.to_csv(csv_file, encoding=\"utf-8\", index=False)\n",
|
1589 |
+
" path_to_csv = gr.File.update(value=csv_file, visible=True)\n",
|
1590 |
+
"\n",
|
1591 |
+
" return filename, path_to_csv, df"
|
1592 |
+
],
|
1593 |
+
"metadata": {
|
1594 |
+
"id": "vmxXLQRyJn00"
|
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+
},
|
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"execution_count": 12,
|
1597 |
+
"outputs": []
|
1598 |
+
},
|
1599 |
+
{
|
1600 |
+
"cell_type": "code",
|
1601 |
+
"source": [
|
1602 |
+
"## Gradio interface\n",
|
1603 |
+
"headers = [\"start\", \"end\", \"text\"]\n",
|
1604 |
+
"iface = gr.Interface(fn=audio_to_transcript,\n",
|
1605 |
+
" inputs=gr.File(label=\"Audio file\"),\n",
|
1606 |
+
" outputs=[\n",
|
1607 |
+
" gr.Textbox(label=\"Audio file name\"),\n",
|
1608 |
+
" gr.File(label=\"Transcript csv file\"),\n",
|
1609 |
+
" gr.DataFrame(label=\"Transcript\", headers=headers),\n",
|
1610 |
+
" ],\n",
|
1611 |
+
" allow_flagging=\"never\",\n",
|
1612 |
+
" title=\"Audio to Transcript\",\n",
|
1613 |
+
" description=\"Just paste any audio file and get its corresponding transcript with timeline.\",\n",
|
1614 |
+
" )\n",
|
1615 |
+
"iface.launch()"
|
1616 |
+
],
|
1617 |
+
"metadata": {
|
1618 |
+
"id": "vYm0qelyU1F2"
|
1619 |
+
},
|
1620 |
+
"execution_count": null,
|
1621 |
+
"outputs": []
|
1622 |
+
},
|
1623 |
+
{
|
1624 |
+
"cell_type": "markdown",
|
1625 |
+
"source": [
|
1626 |
+
"# END"
|
1627 |
+
],
|
1628 |
+
"metadata": {
|
1629 |
+
"id": "u9QYxqjtnzCD"
|
1630 |
+
}
|
1631 |
+
}
|
1632 |
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]
|
1633 |
+
}
|