added gevent for websockets server and reworked main structure
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@ -1,16 +1,15 @@
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import json
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import logging
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import sys
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import tempfile
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from threading import Lock
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import openai
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import requests
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from flask import Flask, request
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from flask_socketio import SocketIO, emit, join_room, leave_room, \
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rooms
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from pywhispercpp.model import Model
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from jarvis.utils.chatgpt_utils import chatgpt_recognise
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from jarvis.utils import chat_utils, whisper_utils
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# Set this variable to "threading", "eventlet" or "gevent" to test the
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# different async modes, or leave it set to None for the application to choose
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@ -22,7 +21,7 @@ app.config['SECRET_KEY'] = 'secret!'
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socketio = SocketIO(app, async_mode=async_mode)
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thread = None
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thread_lock = Lock()
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model = Model('base', n_threads=16, suppress_non_speech_tokens=True)
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openai.api_key = sys.argv[1]
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@ -34,25 +33,34 @@ def index():
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@socketio.event
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def process_message(message):
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message = json.loads(message)
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print("New PROCESS request from room " + message['uuid'])
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logging.info("New PROCESS request from room " + message['uuid'])
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logging.info("Message : " + message['data'])
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print("Message : " + message['data'])
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# TODO: maybe implement grammar check ?
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# TODO: maybe implement grammar check and correction ?
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# intent_manager.recognise(message['data'], message['uuid'])
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send_jarvis_message_to_room("I don't know how to respond to that...", message['uuid'])
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response = chatgpt_recognise(message['data'])
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if 'comment' in response:
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send_user_message_to_room(response['comment'], message['uuid'])
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else:
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send_jarvis_message_to_room("I don't know how to respond to that...", message['uuid'])
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if message['data'] != "":
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# response = chatgpt_recognise(message['data'])
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response = {'action': 'answer',
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'answer': "Hello! As an AI, I don't have emotions, but I'm always here to help you with your smart home needs. How can I assist you today?"}
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if response['action'] == 'clarify':
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chat_utils.send_jarvis_message_to_room(response['question'], message['uuid'])
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elif response['action'] == 'command':
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chat_utils.send_jarvis_message_to_room(response['comment'], message['uuid'])
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elif response['action'] == 'query':
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chat_utils.send_jarvis_message_to_room(response['device_description'], message['uuid'])
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elif response['action'] == 'answer':
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chat_utils.send_jarvis_message_to_room(response['answer'], message['uuid'])
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else:
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chat_utils.send_jarvis_message_to_room("I don't know how to respond to that...", message['uuid'])
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@socketio.event
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def join(message):
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message = json.loads(message)
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print("New client joined room " + message['uuid'])
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logging.info("New client joined room " + message['uuid'])
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join_room(message['uuid'])
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@ -68,27 +76,18 @@ def connect():
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emit('my_response', {'data': 'Connected', 'count': 0})
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def send_user_message_to_room(text, room_id):
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socketio.emit('message_from_user', {'data': text, "uuid": room_id}, to=room_id)
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def send_jarvis_message_to_room(text, room_id):
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socketio.emit('message_from_jarvis', {'data': text, "uuid": room_id}, to=room_id)
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# .WAV (i.e.) FILE REQUEST
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@app.route("/get_text_from_audio", methods=['POST'])
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def get_text_from_audio():
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print("[" + request.remote_addr + "] - New STT request")
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logging.info("New STT request from " + request.remote_addr)
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audio_temp_file = tempfile.NamedTemporaryFile(prefix='jarvis-audio_', suffix='_client')
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audio_temp_file.write(request.data)
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# text = whisper_stt(audio_temp_file.name)
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text = whisper_cpp_stt(audio_temp_file.name)
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print(text)
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text = whisper_utils.whisper_cpp_stt(audio_temp_file.name)
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logging.info("STT result for " + request.remote_addr + " : " + text)
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return {"data": text, "uuid": "null"}
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return {"data": text}
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"""
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@ -103,39 +102,6 @@ def process_text():
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return {"transcription": text, "answer": answer}"""
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# send request to whisper-asr server (docker)
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def whisper_stt(audio_file):
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headers = {
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'accept': 'application/json',
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# 'Content-Type': 'multipart/form-data',
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}
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params = {
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'task': 'transcribe',
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# TODO: add to config
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'language': 'fr',
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'output': 'json',
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}
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files = {
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'audio_file': open(audio_file, 'rb'),
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}
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# TODO: add to config
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response = requests.post('https://whisper.broillet.ch/asr', params=params, headers=headers, files=files)
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return json.loads(response.text)['text']
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def whisper_cpp_stt(audio_file):
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segments = model.transcribe(audio_file, speed_up=False, translate=False)
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# combines all segments in one string
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text = ''
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for segment in segments:
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text += segment.text + ' '
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return text
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def start_server():
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socketio.run(app, host='0.0.0.0', port=6000, allow_unsafe_werkzeug=True)
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def start_api():
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logging.info("Starting Jarvis Server API...")
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socketio.run(app, host='0.0.0.0', port=6000)
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@ -1,10 +1,14 @@
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import api
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import logging
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import lingua_franca
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import api
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from jarvis.skills.cocktails import CocktailSkill
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from jarvis.skills.intent_services import intent_manager
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from jarvis.utils import whisper_utils
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if __name__ == '__main__':
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logging.getLogger().setLevel(logging.DEBUG)
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# Load lingua franca in the memory
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lingua_franca.load_language(lang="fr")
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@ -15,5 +19,8 @@ if __name__ == '__main__':
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# Load the skills
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intent_manager.load_all_skills()
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# Load the STT (whisper) model
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whisper_utils.load_model()
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# Start the api endpoint
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api.start_server()
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api.start_api()
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13
jarvis/utils/chat_utils.py
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13
jarvis/utils/chat_utils.py
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@ -0,0 +1,13 @@
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import logging
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from jarvis.api import socketio
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def send_user_message_to_room(text, room_id):
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logging.debug("Sending message from user to room " + room_id + " : " + text)
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socketio.emit('message_from_user', {'data': text, "uuid": room_id}, to=room_id)
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def send_jarvis_message_to_room(text, room_id):
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logging.debug("Sending message from jarvis to room " + room_id + " : " + text)
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socketio.emit('message_from_jarvis', {'data': text, "uuid": room_id}, to=room_id)
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jarvis/utils/whisper_utils.py
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69
jarvis/utils/whisper_utils.py
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@ -0,0 +1,69 @@
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import json
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import logging
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import requests
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from pywhispercpp.model import Model
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from jarvis.utils import languages_utils
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def load_model():
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log_level = logging.getLogger().level
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global model
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model = Model('base', n_threads=8, suppress_non_speech_tokens=True, log_level=logging.ERROR)
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logging.getLogger().setLevel(log_level)
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def get_model():
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return model
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def whisper_cpp_stt(audio_file):
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"""
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Transcribe audio file using whisper-cpp, no additional server/service needed, runs on CPU.
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:param audio_file:
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:param model:
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:return: text
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"""
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if model is None:
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logging.error("Model is not loaded")
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load_model()
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segments = model.transcribe(audio_file, speed_up=False, translate=False)
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# combines all segments in one string
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text = ''
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for segment in segments:
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text += segment.text + ' '
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return text
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def whisper_asr_stt(audio_file):
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"""
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Transcribe audio file using whisper-asr (docker), a server is needed, runs on GPU.
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See : https://github.com/ahmetoner/whisper-asr-webservice
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:param audio_file:
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:return: text
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"""
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headers = {
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'accept': 'application/json',
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# 'Content-Type': 'multipart/form-data',
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}
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params = {
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'task': 'transcribe',
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# TODO: add to config
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'language': languages_utils.get_language(),
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'output': 'json',
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}
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files = {
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'audio_file': open(audio_file, 'rb'),
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}
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# TODO: add to config
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response = requests.post('https://whisper.broillet.ch/asr', params=params, headers=headers, files=files)
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return json.loads(response.text)['text']
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Flask-SocketIO
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pywhispercpp
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padatious
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openai
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openai
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gevent
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gevent-websocket
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