File size: 9,509 Bytes
63e8a17
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ed28876
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
# 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 queue
import sys
import subprocess
import tempfile
import threading
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 = []
#    stop_recording = threading.Event()
#    audio_queue = queue.Queue()

    def audio_callback():
        for _ in range(0, int(sample_rate / chunk_size * duration)):
            if stop_recording.is_set():
                break
            data = stream.read(chunk_size)
            audio_queue.put(data)

    audio_thread = threading.Thread(target=audio_callback)
    audio_thread.start()

    return p, stream, audio_queue, stop_recording, audio_thread


def stop_recording(p, stream, audio_queue, stop_recording_event, audio_thread):
    stop_recording_event.set()
    audio_thread.join()

    frames = []
    while not audio_queue.empty():
        frames.append(audio_queue.get())

    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

#
#
#######################################################################################################################