Spaces:
Runtime error
Runtime error
File size: 7,313 Bytes
6c3d109 231a341 6c3d109 3380259 6c3d109 0e7c72c 6c3d109 9f5577b 6c3d109 9f5577b 6c3d109 9f5577b 6c3d109 0d11303 6c3d109 3380259 6c3d109 |
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 |
import os
import time
import gradio as gr
from pathlib import Path
import pysrt
import pandas as pd
if os.path.isdir(f'{os.getcwd() + os.sep}whisper.cpp'):
print("Models already loaded")
else:
os.system('git clone https://github.com/ggerganov/whisper.cpp.git')
os.system('git clone https://huggingface.co/Finnish-NLP/Finnish-finetuned-whisper-models-ggml-format')
os.system('make -C ./whisper.cpp')
whisper_models = ["medium", "large"]
whisper_modelpath_translator= {
"medium": "./Finnish-finetuned-whisper-models-ggml-format/ggml-model-fi-medium.bin",
"large": "./Finnish-finetuned-whisper-models-ggml-format/ggml-model-fi-large-v3.bin"
}
def speech_to_text(audio_path, whisper_model):
if(audio_path is None):
retry_cnt = 0
for retry_cnt in range(3):
if(audio_path is None):
print(f'Retrying, retry counter: {retry_cnt +1}')
time.sleep(0.5)
retry_cnt +=1
if retry_cnt == 3:
raise ValueError("Error no audio input")
else:
break
print(audio_path)
try:
_,file_ending = os.path.splitext(f'{audio_path}')
print(f'file enging is {file_ending}')
print("starting conversion to wav")
new_path = audio_path.replace(file_ending, "_converted.wav")
os.system(f'ffmpeg -i "{audio_path}" -ar 16000 -y -ac 1 -c:a pcm_s16le "{new_path}"')
print("conversion to wav ready")
except Exception as e:
raise RuntimeError(f'Error Running inference with local model: {e}') from e
try:
print("starting whisper c++")
srt_path = new_path + ".srt"
os.system(f'rm -f {srt_path}')
os.system(f'./whisper.cpp/main "{new_path}" -t 4 -m ./{whisper_modelpath_translator.get(whisper_model)} -osrt')
print("starting whisper done with whisper")
except Exception as e:
raise RuntimeError(f'Error running Whisper cpp model: {e}') from e
try:
df = pd.DataFrame(columns = ['start','end','text'])
subs = pysrt.open(srt_path)
rows = []
for sub in subs:
start_hours = str(str(sub.start.hours) + "00")[0:2] if len(str(sub.start.hours)) == 2 else str("0" + str(sub.start.hours) + "00")[0:2]
end_hours = str(str(sub.end.hours) + "00")[0:2] if len(str(sub.end.hours)) == 2 else str("0" + str(sub.end.hours) + "00")[0:2]
start_minutes = str(str(sub.start.minutes) + "00")[0:2] if len(str(sub.start.minutes)) == 2 else str("0" + str(sub.start.minutes) + "00")[0:2]
end_minutes = str(str(sub.end.minutes) + "00")[0:2] if len(str(sub.end.minutes)) == 2 else str("0" + str(sub.end.minutes) + "00")[0:2]
start_seconds = str(str(sub.start.seconds) + "00")[0:2] if len(str(sub.start.seconds)) == 2 else str("0" + str(sub.start.seconds) + "00")[0:2]
end_seconds = str(str(sub.end.seconds) + "00")[0:2] if len(str(sub.end.seconds)) == 2 else str("0" + str(sub.end.seconds) + "00")[0:2]
start_millis = str(str(sub.start.milliseconds) + "000")[0:3]
end_millis = str(str(sub.end.milliseconds) + "000")[0:3]
rows.append([sub.text, f'{start_hours}:{start_minutes}:{start_seconds}.{start_millis}', f'{end_hours}:{end_minutes}:{end_seconds}.{end_millis}'])
for row in rows:
srt_to_df = {
'start': [row[1]],
'end': [row[2]],
'text': [row[0]]
}
df = pd.concat([df, pd.DataFrame(srt_to_df)])
except Exception as e:
print(f"Error creating srt df with error: {e}")
return df
def output_to_files(df):
df.reset_index(inplace=True)
print("Starting SRT-file creation")
print(df.head())
with open('subtitles.vtt','w', encoding="utf-8") as file:
print("Starting WEBVTT-file creation")
for i in range(len(df)):
if i == 0:
file.write('WEBVTT')
file.write('\n')
else:
file.write(str(i+1))
file.write('\n')
start = df.iloc[i]['start']
file.write(f"{start.strip()}")
stop = df.iloc[i]['end']
file.write(' --> ')
file.write(f"{stop}")
file.write('\n')
file.writelines(df.iloc[i]['text'])
if int(i) != len(df)-1:
file.write('\n\n')
print("WEBVTT DONE")
with open('subtitles.srt','w', encoding="utf-8") as file:
print("Starting SRT-file creation")
for i in range(len(df)):
file.write(str(i+1))
file.write('\n')
start = df.iloc[i]['start']
file.write(f"{start.strip()}")
stop = df.iloc[i]['end']
file.write(' --> ')
file.write(f"{stop}")
file.write('\n')
file.writelines(df.iloc[i]['text'])
if int(i) != len(df)-1:
file.write('\n\n')
print("SRT DONE")
subtitle_files_out = ['subtitles.vtt','subtitles.srt']
return subtitle_files_out
# ---- Gradio Layout -----
demo = gr.Blocks(css='''
#cut_btn, #reset_btn { align-self:stretch; }
#\\31 3 { max-width: 540px; }
.output-markdown {max-width: 65ch !important;}
''')
demo.encrypt = False
with demo:
with gr.Row():
with gr.Column():
gr.Markdown('''
# Simple Finnish Audio --> Text app
### This space allows you to:
1. Insert audio file or record with microphone
2. Run audio through transcription process using speech recognition models
3. Download generated transcriptions in .vtt and .srt formats
''')
with gr.Row():
with gr.Column():
audio_in = gr.Audio(label="Audio file", type='filepath')
transcribe_btn = gr.Button("Step 1. Transcribe audio")
selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="large", label="Selected Whisper model", interactive=True)
with gr.Row():
with gr.Column():
transcription_df = gr.DataFrame(headers = ['start','end','text'], label="Transcription dataframe")
with gr.Row():
with gr.Column():
translate_transcriptions_button = gr.Button("Step 2. Create subtitle files")
with gr.Row():
with gr.Column():
gr.Markdown('''##### From here you can download subtitles in .srt or .vtt format''')
subtitle_files = gr.File(
label="Download files",
file_count="multiple",
type="filepath",
interactive=False,
)
# Functionalities
transcribe_btn.click(speech_to_text, [audio_in, selected_whisper_model], [transcription_df])
translate_transcriptions_button.click(output_to_files, transcription_df, [subtitle_files])
demo.launch() |