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# Audio_Transcription_Lib.py
#########################################
# Transcription Library
# This library is used to perform transcription of audio files.
# Currently, uses faster_whisper for transcription.
#
####################
# Function List
#
# 1. convert_to_wav(video_file_path, offset=0, overwrite=False)
# 2. speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='small.en', vad_filter=False)
#
####################
#
# Import necessary libraries to run solo for testing
import gc
import json
import logging
import os
import sys
import subprocess
import tempfile
import time
import configparser
# DEBUG Imports
#from memory_profiler import profile
import pyaudio
# Import Local
#
#######################################################################################################################
# Function Definitions
#
# Convert video .m4a into .wav using ffmpeg
# ffmpeg -i "example.mp4" -ar 16000 -ac 1 -c:a pcm_s16le "output.wav"
# https://www.gyan.dev/ffmpeg/builds/
#
whisper_model_instance = None
# Retrieve processing choice from the configuration file
config = configparser.ConfigParser()
config.read('config.txt')
processing_choice = config.get('Processing', 'processing_choice', fallback='cpu')
# FIXME: This is a temporary solution.
# This doesn't clear older models, which means potentially a lot of memory is being used...
def get_whisper_model(model_name, device):
global whisper_model_instance
if whisper_model_instance is None:
from faster_whisper import WhisperModel
logging.info(f"Initializing new WhisperModel with size {model_name} on device {device}")
whisper_model_instance = WhisperModel(model_name, device=device)
return whisper_model_instance
# os.system(r'.\Bin\ffmpeg.exe -ss 00:00:00 -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{out_path}"')
#DEBUG
#@profile
def convert_to_wav(video_file_path, offset=0, overwrite=False):
out_path = os.path.splitext(video_file_path)[0] + ".wav"
if os.path.exists(out_path) and not overwrite:
print(f"File '{out_path}' already exists. Skipping conversion.")
logging.info(f"Skipping conversion as file already exists: {out_path}")
return out_path
print("Starting conversion process of .m4a to .WAV")
out_path = os.path.splitext(video_file_path)[0] + ".wav"
try:
if os.name == "nt":
logging.debug("ffmpeg being ran on windows")
if sys.platform.startswith('win'):
ffmpeg_cmd = ".\\Bin\\ffmpeg.exe"
logging.debug(f"ffmpeg_cmd: {ffmpeg_cmd}")
else:
ffmpeg_cmd = 'ffmpeg' # Assume 'ffmpeg' is in PATH for non-Windows systems
command = [
ffmpeg_cmd, # Assuming the working directory is correctly set where .\Bin exists
"-ss", "00:00:00", # Start at the beginning of the video
"-i", video_file_path,
"-ar", "16000", # Audio sample rate
"-ac", "1", # Number of audio channels
"-c:a", "pcm_s16le", # Audio codec
out_path
]
try:
# Redirect stdin from null device to prevent ffmpeg from waiting for input
with open(os.devnull, 'rb') as null_file:
result = subprocess.run(command, stdin=null_file, text=True, capture_output=True)
if result.returncode == 0:
logging.info("FFmpeg executed successfully")
logging.debug("FFmpeg output: %s", result.stdout)
else:
logging.error("Error in running FFmpeg")
logging.error("FFmpeg stderr: %s", result.stderr)
raise RuntimeError(f"FFmpeg error: {result.stderr}")
except Exception as e:
logging.error("Error occurred - ffmpeg doesn't like windows")
raise RuntimeError("ffmpeg failed")
elif os.name == "posix":
os.system(f'ffmpeg -ss 00:00:00 -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{out_path}"')
else:
raise RuntimeError("Unsupported operating system")
logging.info("Conversion to WAV completed: %s", out_path)
except subprocess.CalledProcessError as e:
logging.error("Error executing FFmpeg command: %s", str(e))
raise RuntimeError("Error converting video file to WAV")
except Exception as e:
logging.error("speech-to-text: Error transcribing audio: %s", str(e))
return {"error": str(e)}
gc.collect()
return out_path
# Transcribe .wav into .segments.json
#DEBUG
#@profile
def speech_to_text(audio_file_path, selected_source_lang='en', whisper_model='medium.en', vad_filter=False, diarize=False):
global whisper_model_instance, processing_choice
logging.info('speech-to-text: Loading faster_whisper model: %s', whisper_model)
time_start = time.time()
if audio_file_path is None:
raise ValueError("speech-to-text: No audio file provided")
logging.info("speech-to-text: Audio file path: %s", audio_file_path)
try:
_, file_ending = os.path.splitext(audio_file_path)
out_file = audio_file_path.replace(file_ending, ".segments.json")
prettified_out_file = audio_file_path.replace(file_ending, ".segments_pretty.json")
if os.path.exists(out_file):
logging.info("speech-to-text: Segments file already exists: %s", out_file)
with open(out_file) as f:
global segments
segments = json.load(f)
return segments
logging.info('speech-to-text: Starting transcription...')
options = dict(language=selected_source_lang, beam_size=5, best_of=5, vad_filter=vad_filter)
transcribe_options = dict(task="transcribe", **options)
# use function and config at top of file
whisper_model_instance = get_whisper_model(whisper_model, processing_choice)
segments_raw, info = whisper_model_instance.transcribe(audio_file_path, **transcribe_options)
segments = []
for segment_chunk in segments_raw:
chunk = {
"Time_Start": segment_chunk.start,
"Time_End": segment_chunk.end,
"Text": segment_chunk.text
}
logging.debug("Segment: %s", chunk)
segments.append(chunk)
if segments:
segments[0]["Text"] = f"This text was transcribed using whisper model: {whisper_model}\n\n" + segments[0]["Text"]
if not segments:
raise RuntimeError("No transcription produced. The audio file may be invalid or empty.")
logging.info("speech-to-text: Transcription completed in %.2f seconds", time.time() - time_start)
# Save the segments to a JSON file - prettified and non-prettified
# FIXME so this is an optional flag to save either the prettified json file or the normal one
save_json = True
if save_json:
logging.info("speech-to-text: Saving segments to JSON file")
output_data = {'segments': segments}
logging.info("speech-to-text: Saving prettified JSON to %s", prettified_out_file)
with open(prettified_out_file, 'w') as f:
json.dump(output_data, f, indent=2)
logging.info("speech-to-text: Saving JSON to %s", out_file)
with open(out_file, 'w') as f:
json.dump(output_data, f)
logging.debug(f"speech-to-text: returning {segments[:500]}")
gc.collect()
return segments
except Exception as e:
logging.error("speech-to-text: Error transcribing audio: %s", str(e))
raise RuntimeError("speech-to-text: Error transcribing audio")
def record_audio(duration, sample_rate=16000, chunk_size=1024):
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=sample_rate,
input=True,
frames_per_buffer=chunk_size)
print("Recording...")
frames = []
for _ in range(0, int(sample_rate / chunk_size * duration)):
data = stream.read(chunk_size)
frames.append(data)
print("Recording finished.")
stream.stop_stream()
stream.close()
p.terminate()
return b''.join(frames)
def save_audio_temp(audio_data, sample_rate=16000):
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
import wave
wf = wave.open(temp_file.name, 'wb')
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(sample_rate)
wf.writeframes(audio_data)
wf.close()
return temp_file.name
#
#
####################################################################################################################### |