Create app.py

app.py

@@ -0,0 +1,140 @@
+import ffmpy
+import asyncio
+import edge_tts
+import subprocess
+import gradio as gr
+import translators as ts
+from gradio_client import Client
+from list_dict import translates, speakers
+
+translate = translates
+language = translate[list(translate.keys())[9]]
+speaker = speakers
+
+file_name = 'audio'
+main_video = 'video.mp4'
+main_audio = f'{file_name}.wav'
+folder = 'output_directory'
+text_to_speech = 'text_to_speech.wav'
+vocals = f'./{folder}/{file_name}/vocals.wav'
+vocals_monorail = f'./{folder}/{file_name}/vocals_monorail.wav'
+accompaniment = f'./{folder}/{file_name}/accompaniment.wav'
+output_rate_audio = 'output_rate_audio.wav'
+output_audio = 'output.wav'
+output_video = 'output.mp4'
+
+def gain_time(audio):
+  command = ['ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1', audio]
+  result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+  return float(result.stdout)
+
+def time_verify():
+  audios = [vocals_monorail, text_to_speech]
+  time_lists = []
+
+  for audio in audios:
+    time_lists.append(gain_time(audio))
+
+  r_time = float(max(time_lists)) / float(min(time_lists))
+  return r_time
+
+def translator(text, TR_LANGUAGE, LANGUAGE):
+  ts_text = ts.translate_text(text, translator=TR_LANGUAGE, from_language='auto', to_language=language[LANGUAGE])
+  return ts_text
+
+def video_inputs(video, TR_LANGUAGE, LANGUAGE, SPEAKER):
+  language = translate[TR_LANGUAGE]
+
+  if video is None:
+      raise gr.Error('No audio file submitted!')
+  elif language is None:
+      raise gr.Error('Please select google translator!')
+  elif TR_LANGUAGE != 'google':
+      raise gr.Error('Language has been reloaded, please select again!')
+  elif float(gain_time(video)) > 60:
+      raise gr.Error('Exceed maximum limit!')
+
+  ff = ffmpy.FFmpeg(
+      inputs={
+          video: None
+          },
+      outputs={
+          main_video: ['-y', '-map', '0:0', '-c:a', 'copy', '-f', 'mp4'],
+          main_audio: ['-y', '-map', '0:a', '-vn', '-acodec', 'pcm_s16le', '-ar', '16000', '-ac', '1', '-f', 'wav']
+          }
+      )
+  ff.run()
+
+  subprocess.run(['spleeter', 'separate', '-o', folder, '-p', 'spleeter:2stems-16kHz', main_audio])
+
+  ff = ffmpy.FFmpeg(
+      inputs={
+          vocals: None
+          },
+      outputs={
+          vocals_monorail: ['-y', '-i', vocals, '-vn', '-acodec', 'pcm_s16le', '-ar', '16000', '-ac', '1', '-f', 'wav']
+          }
+      )
+  ff.run()
+
+  client = Client('https://hf-audio-whisper-large-v3.hf.space/')
+  result = client.predict(
+          vocals_monorail,	# str (filepath or URL to file) in 'inputs' Audio component
+          'transcribe',	# str in 'Task' Radio component
+          api_name='/predict'
+  )
+
+  ts_text = translator(result, TR_LANGUAGE, LANGUAGE)
+
+  async def amain():
+    communicate = edge_tts.Communicate(ts_text, SPEAKER)
+    await communicate.save(text_to_speech)
+  asyncio.run(amain())
+
+  rate_audio = time_verify()
+  ff = ffmpy.FFmpeg(
+      inputs={text_to_speech: None},
+      outputs={
+          output_rate_audio: ['-y', '-filter:a', f'atempo={rate_audio}']
+      }
+  )
+  ff.run()
+
+  ff = ffmpy.FFmpeg(
+      inputs={
+          output_rate_audio: None,
+          accompaniment: None
+      },
+      outputs={
+          output_audio: '-y -filter_complex amix=inputs=2'
+      }
+  )
+  ff.run()
+
+  ff = ffmpy.FFmpeg(
+      inputs={output_audio: None, main_video: None},
+      outputs={output_video: '-y -c:v copy -c:a aac -strict experimental'}
+  )
+  ff.run()
+
+  return output_video
+
+with gr.Blocks() as demo:
+  TR_LANGUAGE = gr.Dropdown(translate, value=list(translate.keys())[9], label='Translator')
+  LANGUAGE = gr.Dropdown(language, value=list(language.keys())[0], label='Language')
+  SPEAKER = gr.Dropdown(speaker, value=speaker[0], label='Speaker')
+  gr.Interface(
+      fn=video_inputs,
+      inputs=[
+          gr.Video(height=320, width=600, interactive=True, label='Input_video'),
+          TR_LANGUAGE,
+          LANGUAGE,
+          SPEAKER,
+          ],
+      outputs=[
+          gr.Video(height=320, width=600, label='Output_video'),
+      ],
+      title="Short-Video-To-Video", 
+      description="🤗 [whisper-large-v3](https://huggingface.co/spaces/hf-audio/whisper-large-v3), Limited the video length to 60 seconds. Please check [here](https://github.com/DUQIA/Short-Video-To-Video) for details."
+  )
+demo.launch()