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https://github.com/open-webui/open-webui
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feat: compress audio
Co-Authored-By: Beck Bekmyradov <47065940+bekmuradov@users.noreply.github.com>
This commit is contained in:
Timothy J. Baek
parent
8206c47a47
commit
7152af949b
@ -5,6 +5,8 @@ import os
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import uuid
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from functools import lru_cache
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from pathlib import Path
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from pydub import AudioSegment
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from pydub.silence import split_on_silence
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import requests
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from open_webui.config import (
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@ -35,7 +37,12 @@ from fastapi import Depends, FastAPI, File, HTTPException, Request, UploadFile,
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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from open_webui.utils.utils import get_admin_user, get_current_user, get_verified_user
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from open_webui.utils.utils import get_admin_user, get_verified_user
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# Constants
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MAX_FILE_SIZE_MB = 25
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MAX_FILE_SIZE = MAX_FILE_SIZE_MB * 1024 * 1024 # Convert MB to bytes
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["AUDIO"])
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@ -353,10 +360,103 @@ async def speech(request: Request, user=Depends(get_verified_user)):
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)
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def transcribe(file_path):
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print("transcribe", file_path)
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filename = os.path.basename(file_path)
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file_dir = os.path.dirname(file_path)
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id = filename.split(".")[0]
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if app.state.config.STT_ENGINE == "":
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from faster_whisper import WhisperModel
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whisper_kwargs = {
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"model_size_or_path": WHISPER_MODEL,
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"device": whisper_device_type,
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"compute_type": "int8",
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"download_root": WHISPER_MODEL_DIR,
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"local_files_only": not WHISPER_MODEL_AUTO_UPDATE,
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}
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log.debug(f"whisper_kwargs: {whisper_kwargs}")
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try:
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model = WhisperModel(**whisper_kwargs)
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except Exception:
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log.warning(
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"WhisperModel initialization failed, attempting download with local_files_only=False"
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)
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whisper_kwargs["local_files_only"] = False
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model = WhisperModel(**whisper_kwargs)
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segments, info = model.transcribe(file_path, beam_size=5)
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log.info(
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"Detected language '%s' with probability %f"
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% (info.language, info.language_probability)
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)
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transcript = "".join([segment.text for segment in list(segments)])
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data = {"text": transcript.strip()}
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# save the transcript to a json file
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transcript_file = f"{file_dir}/{id}.json"
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with open(transcript_file, "w") as f:
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json.dump(data, f)
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print(data)
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return data
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elif app.state.config.STT_ENGINE == "openai":
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if is_mp4_audio(file_path):
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print("is_mp4_audio")
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os.rename(file_path, file_path.replace(".wav", ".mp4"))
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# Convert MP4 audio file to WAV format
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convert_mp4_to_wav(file_path.replace(".wav", ".mp4"), file_path)
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headers = {"Authorization": f"Bearer {app.state.config.STT_OPENAI_API_KEY}"}
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files = {"file": (filename, open(file_path, "rb"))}
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data = {"model": app.state.config.STT_MODEL}
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print(files, data)
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r = None
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try:
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r = requests.post(
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url=f"{app.state.config.STT_OPENAI_API_BASE_URL}/audio/transcriptions",
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headers=headers,
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files=files,
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data=data,
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)
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r.raise_for_status()
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data = r.json()
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# save the transcript to a json file
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transcript_file = f"{file_dir}/{id}.json"
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with open(transcript_file, "w") as f:
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json.dump(data, f)
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print(data)
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return data
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except Exception as e:
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log.exception(e)
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error_detail = "Open WebUI: Server Connection Error"
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if r is not None:
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try:
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res = r.json()
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if "error" in res:
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error_detail = f"External: {res['error']['message']}"
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except Exception:
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error_detail = f"External: {e}"
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raise error_detail
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@app.post("/transcriptions")
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def transcribe(
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def transcription(
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file: UploadFile = File(...),
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user=Depends(get_current_user),
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user=Depends(get_verified_user),
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):
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log.info(f"file.content_type: {file.content_type}")
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@ -368,111 +468,53 @@ def transcribe(
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try:
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ext = file.filename.split(".")[-1]
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id = uuid.uuid4()
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filename = f"{id}.{ext}"
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contents = file.file.read()
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file_dir = f"{CACHE_DIR}/audio/transcriptions"
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os.makedirs(file_dir, exist_ok=True)
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file_path = f"{file_dir}/{filename}"
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print(filename)
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contents = file.file.read()
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with open(file_path, "wb") as f:
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f.write(contents)
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f.close()
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if app.state.config.STT_ENGINE == "":
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from faster_whisper import WhisperModel
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try:
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if os.path.getsize(file_path) > MAX_FILE_SIZE: # file is bigger than 25MB
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log.debug(f"File size is larger than {MAX_FILE_SIZE_MB}MB")
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audio = AudioSegment.from_file(file_path)
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audio = audio.set_frame_rate(16000).set_channels(1) # Compress audio
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compressed_path = f"{file_dir}/{id}_compressed.opus"
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audio.export(compressed_path, format="opus", bitrate="32k")
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log.debug(f"Compressed audio to {compressed_path}")
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file_path = compressed_path
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whisper_kwargs = {
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"model_size_or_path": WHISPER_MODEL,
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"device": whisper_device_type,
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"compute_type": "int8",
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"download_root": WHISPER_MODEL_DIR,
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"local_files_only": not WHISPER_MODEL_AUTO_UPDATE,
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}
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log.debug(f"whisper_kwargs: {whisper_kwargs}")
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try:
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model = WhisperModel(**whisper_kwargs)
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except Exception:
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log.warning(
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"WhisperModel initialization failed, attempting download with local_files_only=False"
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)
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whisper_kwargs["local_files_only"] = False
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model = WhisperModel(**whisper_kwargs)
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segments, info = model.transcribe(file_path, beam_size=5)
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log.info(
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"Detected language '%s' with probability %f"
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% (info.language, info.language_probability)
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)
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transcript = "".join([segment.text for segment in list(segments)])
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data = {"text": transcript.strip()}
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# save the transcript to a json file
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transcript_file = f"{file_dir}/{id}.json"
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with open(transcript_file, "w") as f:
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json.dump(data, f)
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print(data)
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if (
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os.path.getsize(file_path) > MAX_FILE_SIZE
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): # Still larger than 25MB after compression
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chunks = split_on_silence(
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audio, min_silence_len=500, silence_thresh=-40
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)
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texts = []
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for i, chunk in enumerate(chunks):
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chunk_file_path = f"{file_dir}/{id}_chunk{i}.{ext}"
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chunk.export(chunk_file_path, format=ext)
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text = transcribe(chunk_file_path)
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texts.append(text)
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data = {"text": " ".join(texts)}
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else:
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data = transcribe(file_path)
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else:
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data = transcribe(file_path)
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return data
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elif app.state.config.STT_ENGINE == "openai":
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if is_mp4_audio(file_path):
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print("is_mp4_audio")
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os.rename(file_path, file_path.replace(".wav", ".mp4"))
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# Convert MP4 audio file to WAV format
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convert_mp4_to_wav(file_path.replace(".wav", ".mp4"), file_path)
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headers = {"Authorization": f"Bearer {app.state.config.STT_OPENAI_API_KEY}"}
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files = {"file": (filename, open(file_path, "rb"))}
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data = {"model": app.state.config.STT_MODEL}
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print(files, data)
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r = None
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try:
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r = requests.post(
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url=f"{app.state.config.STT_OPENAI_API_BASE_URL}/audio/transcriptions",
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headers=headers,
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files=files,
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data=data,
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)
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r.raise_for_status()
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data = r.json()
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# save the transcript to a json file
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transcript_file = f"{file_dir}/{id}.json"
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with open(transcript_file, "w") as f:
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json.dump(data, f)
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print(data)
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return data
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except Exception as e:
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log.exception(e)
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error_detail = "Open WebUI: Server Connection Error"
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if r is not None:
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try:
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res = r.json()
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if "error" in res:
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error_detail = f"External: {res['error']['message']}"
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except Exception:
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error_detail = f"External: {e}"
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raise HTTPException(
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status_code=r.status_code if r != None else 500,
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detail=error_detail,
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)
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except Exception as e:
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log.exception(e)
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=ERROR_MESSAGES.DEFAULT(e),
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)
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except Exception as e:
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log.exception(e)
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@ -700,7 +700,7 @@
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childrenIds: [],
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role: 'user',
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content: userPrompt,
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files: chatFiles.length > 0 ? chatFiles : undefined,
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files: _files.length > 0 ? _files : undefined,
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timestamp: Math.floor(Date.now() / 1000), // Unix epoch
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models: selectedModels
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};
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@ -54,7 +54,7 @@
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</div>
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<div>
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<div class="flex flex-col md:flex-row gap-1 justify-between w-full">
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<div class="flex flex-col items-center md:flex-row gap-1 justify-between w-full">
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<div class=" flex flex-wrap text-sm gap-1 text-gray-500">
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{#if file.size}
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<div class="capitalize shrink-0">{formatFileSize(file.size)}</div>
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