Skip to content

Instantly share code, notes, and snippets.

Created March 20, 2024 19:58
Show Gist options
  • Save afrendeiro/65dc8066739227a5fc636cbb6e8b12e9 to your computer and use it in GitHub Desktop.
Save afrendeiro/65dc8066739227a5fc636cbb6e8b12e9 to your computer and use it in GitHub Desktop.
#! python3.7
import argparse
import os
import numpy as np
import speech_recognition as sr
import whisper
import torch
from datetime import datetime, timedelta
from queue import Queue
from time import sleep
from sys import platform
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", default="medium", help="Model to use",
choices=["tiny", "base", "small", "medium", "large"])
parser.add_argument("--non_english", action='store_true',
help="Don't use the english model.")
parser.add_argument("--energy_threshold", default=1000,
help="Energy level for mic to detect.", type=int)
parser.add_argument("--record_timeout", default=1,
help="How real time the recording is in seconds.", type=float)
parser.add_argument("--phrase_timeout", default=10,
help="How much empty space between recordings before we "
"consider it a new line in the transcription.", type=float)
if 'linux' in platform:
parser.add_argument("--default_microphone", default='pulse',
help="Default microphone name for SpeechRecognition. "
"Run this with 'list' to view available Microphones.", type=str)
args = parser.parse_args()
# The last time a recording was retrieved from the queue.
phrase_time = None
# Thread safe Queue for passing data from the threaded recording callback.
data_queue = Queue()
# We use SpeechRecognizer to record our audio because it has a nice feature where it can detect when speech ends.
recorder = sr.Recognizer()
recorder.energy_threshold = args.energy_threshold
# Definitely do this, dynamic energy compensation lowers the energy threshold dramatically to a point
# where the SpeechRecognizer never stops recording.
recorder.dynamic_energy_threshold = False
# Important for linux users.
# Prevents permanent application hang and crash by using the wrong Microphone
if 'linux' in platform:
mic_name = args.default_microphone
if not mic_name or mic_name == 'list':
print("Available microphone devices are: ")
for index, name in enumerate(sr.Microphone.list_microphone_names()):
print(f"Microphone with name \"{name}\" found")
for index, name in enumerate(sr.Microphone.list_microphone_names()):
if mic_name in name:
source = sr.Microphone(sample_rate=16000, device_index=index)
source = sr.Microphone(sample_rate=16000)
# Load / Download model
model = args.model
if args.model != "large" and not args.non_english:
model = model + ".en"
audio_model = whisper.load_model(model)
record_timeout = args.record_timeout
phrase_timeout = args.phrase_timeout
transcription = ['']
with source:
def record_callback(_, audio: sr.AudioData) -> None:
Threaded callback function to receive audio data when recordings finish.
audio: An AudioData containing the recorded bytes.
# Grab the raw bytes and push it into the thread safe queue.
data = audio.get_raw_data()
# Create a background thread that will pass us raw audio bytes.
# We could do this manually but SpeechRecognizer provides a nice helper.
recorder.listen_in_background(source, record_callback, phrase_time_limit=record_timeout)
# Cue the user that we're ready to go.
print("Model loaded.\n")
while True:
now = datetime.utcnow()
# Pull raw recorded audio from the queue.
if not data_queue.empty():
phrase_complete = False
# If enough time has passed between recordings, consider the phrase complete.
# Clear the current working audio buffer to start over with the new data.
if phrase_time and now - phrase_time > timedelta(seconds=phrase_timeout):
phrase_complete = True
# This is the last time we received new audio data from the queue.
phrase_time = now
# Combine audio data from queue
audio_data = b''.join(data_queue.queue)
# Convert in-ram buffer to something the model can use directly without needing a temp file.
# Convert data from 16 bit wide integers to floating point with a width of 32 bits.
# Clamp the audio stream frequency to a PCM wavelength compatible default of 32768hz max.
audio_np = np.frombuffer(audio_data, dtype=np.int16).astype(np.float32) / 32768.0
# Read the transcription.
result = audio_model.transcribe(audio_np, fp16=torch.cuda.is_available())
text = result['text'].strip()
# If we detected a pause between recordings, add a new item to our transcription.
# Otherwise edit the existing one.
if phrase_complete:
transcription[-1] = text
# Clear the console to reprint the updated transcription.
os.system('cls' if == 'nt' else 'clear')
for line in transcription:
# Flush stdout.
print('', end='', flush=True)
# Infinite loops are bad for processors, must sleep.
except KeyboardInterrupt:
for line in transcription:
if __name__ == "__main__":
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment