update app 2

app.py

@@ -5,16 +5,17 @@ import re
 from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
 from datasets import load_dataset
 import torch
-import random
-import string
 import soundfile as sf
 import boto3
 from io import BytesIO
 import os
+import botocore
+from time import sleep
 
 AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
 AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
 S3_BUCKET_NAME = os.getenv("BUCKET_NAME")
+FOLDER = 'public/mdx/'
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 # load the processor
@@ -40,20 +41,21 @@ speakers = {
     'slt': 6799   # US female
 }
 
+
 def generateAudio(text_to_audio, s3_save_as, key_id):
 
     if AWS_ACCESS_KEY_ID != key_id:
         return "not permition"
-            
+
     s3_save_as = '-'.join(s3_save_as.split()) + ".wav"
-    
+
     def cut_text(text, max_tokens=500):
         # Remove non-alphanumeric characters, except periods and commas
         text = re.sub(r"[^\w\s.,]", "", text)
-    
+
         # Replace multiple spaces with a single space
         text = re.sub(r"\s{2,}", " ", text)
-    
+
         # Remove line breaks
         text = re.sub(r"\n", " ", text)
 
@@ -74,37 +76,103 @@ def generateAudio(text_to_audio, s3_save_as, key_id):
     def save_text_to_speech(text, speaker=None):
         # Preprocess text and recortar
         text = cut_text(text, max_tokens=500)
-        
+
         # Divide el texto en segmentos de 30 palabras
         palabras = text.split()
-        segmentos = [' '.join(palabras[i:i+30]) for i in range(0, len(palabras), 30)]
-        
+        segmentos = [' '.join(palabras[i:i+30])
+                     for i in range(0, len(palabras), 30)]
+
         # Generar audio para cada segmento y combinarlos
         audio_segments = []
         for segment in segmentos:
             inputs = processor(text=segment, return_tensors="pt").to(device)
             if speaker is not None:
-                speaker_embeddings = torch.tensor(embeddings_dataset[speaker]["xvector"]).unsqueeze(0).to(device)
+                speaker_embeddings = torch.tensor(
+                    embeddings_dataset[speaker]["xvector"]).unsqueeze(0).to(device)
             else:
                 speaker_embeddings = torch.randn((1, 512)).to(device)
-            speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+            speech = model.generate_speech(
+                inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
             audio_segments.append(speech)
-        
+
         combined_audio = torch.cat(audio_segments, dim=0)
-        
+
         # Crear objeto BytesIO para almacenar el audio
         audio_buffer = BytesIO()
-        sf.write(audio_buffer, combined_audio.cpu().numpy(), samplerate=16000, format='WAV')
+        sf.write(audio_buffer, combined_audio.cpu().numpy(),
+                 samplerate=16000, format='WAV')
         audio_buffer.seek(0)
-        
+
         # Guardar el audio combinado en S3
         save_audio_to_s3(audio_buffer)
-    
-    
+
     save_text_to_speech(text_to_audio, 2271)
     return s3_save_as
-    
 
-iface = gr.Interface(fn=generateAudio, inputs=[Textbox(label="text_to_audio"), Textbox(label="S3url"), Textbox(label="aws_key_id")], outputs="text", title="Text-to-Audio")
-iface.launch()
 
+def check_if_exist(bucket_name, key):
+
+    s3 = boto3.resource('s3',
+                        aws_access_key_id=AWS_ACCESS_KEY_ID,
+                        aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
+
+    try:
+        s3.Object(bucket_name, key).load()
+    except botocore.exceptions.ClientError as e:
+        if e.response['Error']['Code'] == "404":
+            # The object does not exist.
+            return False
+        else:
+            # Something else has gone wrong.
+            raise
+    else:
+        return True
+
+
+def list_s3_files():
+
+    s3_client = boto3.client('s3',
+                             aws_access_key_id=AWS_ACCESS_KEY_ID,
+                             aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
+
+    s3 = boto3.resource('s3',
+                        aws_access_key_id=AWS_ACCESS_KEY_ID,
+                        aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
+
+    my_bucket = s3.Bucket(S3_BUCKET_NAME)
+
+    for objects in my_bucket.objects.filter(Prefix=FOLDER):
+        print(objects.key)
+
+        filename_ext = '%s' % os.path.basename(objects.key)
+        filename = os.path.splitext(filename_ext)[0]
+        s3audio = 'public/%s.wav' % filename
+
+        if check_if_exist(S3_BUCKET_NAME, s3audio):
+            print('Audio %s already exists!' % s3audio)
+        else:
+            response = s3_client.head_object(
+                Bucket=S3_BUCKET_NAME, Key=objects.key)
+            metadata = response['Metadata']
+            print(metadata)
+            if 'titulo' in metadata:
+                print('Has titulo, ready to create Audio!')
+                print('Start creating audio.. %s ' % s3audio)
+                title = metadata['titulo']
+                generateAudio(title, filename, AWS_ACCESS_KEY_ID)
+            else:
+                print('There is NOT resume, skipping..')
+
+        sleep(500/1000)
+
+
+demo = gr.Blocks()
+with demo:
+
+    text = gr.Textbox()
+
+    bimage = gr.Button("Generate Blog Images for PineSearch!")
+
+    bimage.click(list_s3_files, outputs=text)
+
+demo.launch()