Update app.py

app.py

@@ -259,15 +259,18 @@ pdf_path=os.path.join(os.path.abspath(""), "hidden-technical-debt-in-machine-lea
 pdf_path2=os.path.join(os.path.abspath(""), "1812_05944.pdf")
 
 
-
-
-
-
 # =======================================
 # 
 # ======================================= 
 def sentence_to_audio(fileobj):
+    from transformers import pipeline, AutoProcessor, AutoModel
+    import torch
+    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+    from transformers import pipeline 
+    import numpy as np
+    import scipy
 
+    
     # text mining from pdf
     text_per_page = read_pdf(fileobj.name)
     text_per_page.keys()
@@ -293,15 +296,30 @@ def sentence_to_audio(fileobj):
     summary_text=summary[0].get("summary_text")
     
     # Sentence 2 Speech
-    processor = AutoProcessor.from_pretrained("suno/bark-small")
-    model = AutoModel.from_pretrained("suno/bark-small")
-    inputs = processor(
-        text=summary_text,
-        return_tensors="pt",
-    )
-    speech_values = model.generate(**inputs, do_sample=True)
-    sampling_rate = model.generation_config.sample_rate
-    return sampling_rate, speech_values.cpu().numpy().squeeze(),summary_text
+
+
+    #txt1="Hello ->>          " + fileobj.name + "        <<!"
+    #txt1="Hello"
+    #txt2="ciccio"
+
+    # Sentence 2 Speech
+    s_to_s = pipeline("text-to-speech", model="suno/bark-small")
+    generated_audio=s_to_s(summary_text,forward_params={"do_sample": True})
+    scipy.io.wavfile.write("s_2_s.wav", rate=generated_audio["sampling_rate"], data=generated_audio["audio"].T)
+    return "s_2_s.wav",summary_text    
+
+
+
+    
+#    processor = AutoProcessor.from_pretrained("suno/bark-small")
+#    model = AutoModel.from_pretrained("suno/bark-small")
+#    inputs = processor(
+#        text=summary_text,
+#        return_tensors="pt",
+#    )
+#    speech_values = model.generate(**inputs, do_sample=True)
+#    sampling_rate = model.generation_config.sample_rate
+#    return sampling_rate, speech_values.cpu().numpy().squeeze(),summary_text
 
 
 # ============================================================================================