Update app.py

app.py

@@ -1,30 +1,24 @@
 import gradio as gr
-# import os, subprocess, torchaudio
-# import torch
-from PIL import Image
-from gtts import gTTS
-import tempfile
-from pydub import AudioSegment
-from pydub.generators import Sine
-# from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
-# from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
-import soundfile
-
-import dlib
-import cv2
-import imageio
-import os
-import gradio as gr
 import os, subprocess, torchaudio
+import torch
 from PIL import Image
-import ffmpeg
-
-
 
 block = gr.Blocks()
 
+def pad_image(image):
+    w, h = image.size
+    if w == h:
+        return image
+    elif w > h:
+        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
+        new_image.paste(image, (0, (w - h) // 2))
+        return new_image
+    else:
+        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
+        new_image.paste(image, ((h - w) // 2, 0))
+        return new_image
+
 def calculate(image_in, audio_in):
-    print("in calculate")
     waveform, sample_rate = torchaudio.load(audio_in)
     waveform = torch.mean(waveform, dim=0, keepdim=True)
     torchaudio.save("/content/audio.wav", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
@@ -40,66 +34,23 @@ def calculate(image_in, audio_in):
     os.system(f"cd /content/one-shot-talking-face && python3 -B test_script.py --img_path /content/image.png --audio_path /content/audio.wav --phoneme_path /content/test.json --save_dir /content/train")
     return "/content/train/image_audio.mp4"
     
-    
-def one_shot(image,input_text,gender):
-    if gender == 'Female' or gender == 'female':
-        tts = gTTS(input_text)
-        with tempfile.NamedTemporaryFile(suffix='.mp3', delete=False) as f:
-            tts.write_to_fp(f)
-            f.seek(0)
-            sound = AudioSegment.from_file(f.name, format="mp3")
-            sound.export("/content/audio.wav", format="wav")
-        return calculate(image,"/content/audio.wav")
-    elif gender == 'Male' or gender == 'male':
-        print(gender)
-        models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
-            "Voicemod/fastspeech2-en-male1",
-          arg_overrides={"vocoder": "hifigan", "fp16": False}
-        )
-        model = models[0].cuda()
-        TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
-        generator = task.build_generator([model], cfg)
-        # next(model.parameters()).device
-        
-        sample = TTSHubInterface.get_model_input(task, input_text)
-        sample["net_input"]["src_tokens"] = sample["net_input"]["src_tokens"].cuda()
-        sample["net_input"]["src_lengths"] = sample["net_input"]["src_lengths"].cuda()
-        sample["speaker"] = sample["speaker"].cuda()
-        
-        wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
-        # soundfile.write("/content/audio_before.wav", wav, rate)
-        soundfile.write("/content/audio_before.wav", wav.cpu().clone().numpy(), rate)
-        cmd='ffmpeg -i /content/audio_before.wav -filter:a "atempo=0.7" -vn /content/audio.wav'
-        os.system(cmd)
-        calculate(image,'audio.wav')
- 
-
-
-def generate_ocr(method,image,gender):
-    return "Hello"
-    
 def run():
   with block:
-  
+    
     with gr.Group():
       with gr.Box():
         with gr.Row().style(equal_height=True):
           image_in = gr.Image(show_label=False, type="filepath")
-          # audio_in = gr.Audio(show_label=False, type='filepath')
-          input_text=gr.Textbox(lines=3, value="Hello How are you?", label="Input Text")
-          gender = gr.Radio(["Female","Male"],value="Female",label="Gender")
-          video_out = gr.Video(label="output")
-          # video_out = gr.Video(show_label=False)
+          audio_in = gr.Audio(show_label=False, type='filepath')
+          video_out = gr.Video(show_label=False)
         with gr.Row().style(equal_height=True):
           btn = gr.Button("Generate")          
 
-    btn.click(one_shot, inputs=[image_in, input_text,gender], outputs=[video_out])
-    # block.queue()
+    
+    btn.click(calculate, inputs=[image_in, audio_in], outputs=[video_out])
+    block.queue()
     block.launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
     run()
 
-
-
-