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from starlette.applications import Starlette
from starlette.routing import Route
from sse_starlette.sse import EventSourceResponse
import uvicorn
import asyncio
import numpy as np
import time
import os
import httpx
from queue import Queue
import logging
from datetime import UTC, datetime, timedelta
from time import sleep
import pickle
import speech_recognition as sr
from audio_utils import get_microphone, get_speech_recognizer, get_all_audio_queue, to_audio_array, AudioChunk
logger = logging.getLogger(__name__)
TRANSCRIBING_SERVER = os.getenv('TRANSCRIBING_SERVER', "http://localhost:3535/transcribe")
def main():
recording_duration = 1
sample_rate = 16000
energy_threshold = 300
data_queue = Queue()
microphone = get_microphone(sample_rate=sample_rate)
speech_recognizer = get_speech_recognizer(energy_threshold=energy_threshold)
with microphone:
speech_recognizer.adjust_for_ambient_noise(source=microphone)
def record_callback(_, audio: sr.AudioData) -> None:
data = audio.get_raw_data()
data_queue.put(data)
speech_recognizer.listen_in_background(source=microphone, callback=record_callback, phrase_time_limit=recording_duration)
print("\n🎤 Microphone is now listening...\n")
prev_audio_array = None
current_audio_chunk = AudioChunk(start_time=datetime.now(tz=UTC))
while True:
try:
now = datetime.now(tz=UTC)
# Pull raw recorded audio from the queue.
if not data_queue.empty():
# Store end time if we're over the recording time limit.
if now - current_audio_chunk.start_time > timedelta(seconds=recording_duration):
current_audio_chunk.end_time = now
# Get audio data from queue
audio_data = get_all_audio_queue(data_queue)
audio_np_array = to_audio_array(audio_data)
if current_audio_chunk.is_complete:
print('start serialize')
if prev_audio_array is not None:
serialized = pickle.dumps(
np.concatenate((
prev_audio_array,
current_audio_chunk.audio_array
))
)
else:
serialized = pickle.dumps(current_audio_chunk.audio_array)
prev_audio_array = current_audio_chunk.audio_array
print('end serialize')
start = time.time()
print('start req')
response = httpx.post(TRANSCRIBING_SERVER, data=serialized)
print('req done', response.text, response.status_code, time.time() - start)
# text = transcribe_model.transcribe(current_audio_chunk.audio_array)
# sentence = Sentence(
# start_time=current_audio_chunk.start_time, end_time=current_audio_chunk.end_time, text=text
# )
current_audio_chunk = AudioChunk(
audio_array=audio_np_array, start_time=datetime.now(tz=UTC)
)
# print(sentence.text) # noqa: T201
else:
current_audio_chunk.update_array(audio_np_array)
# Flush stdout
print("", end="", flush=True) # noqa: T201
# Infinite loops are bad for processors, must sleep.
sleep(0.25)
except KeyboardInterrupt:
current_audio_chunk.end_time = datetime.now(tz=UTC)
if current_audio_chunk.is_complete:
logger.warning("⚠️ Transcribing last chunk...")
# text = transcribe_model.transcribe(current_audio_chunk.audio_array)
# sentence = Sentence(
# start_time=current_audio_chunk.start_time, end_time=current_audio_chunk.end_time, text=text
# )
# print(sentence.text) # noqa: T201
break
async def numbers(minimum, maximum):
for i in range(minimum, maximum + 1):
await asyncio.sleep(0.9)
yield dict(data=i)
async def sse(request):
generator = numbers(1, 5)
return EventSourceResponse(generator)
def test(request):
return "hello world"
routes = [
Route('/', endpoint=test),
Route("/test", endpoint=sse)
]
app = Starlette(debug=True, routes=routes)
def server():
uvicorn.run(app, host="0.0.0.0", port=8343, log_level='info')
if __name__ == '__main__':
if os.getenv('PROCESS') == 'listener':
main()
elif os.getenv('PROCESS') == 'server':
server()
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