Update app.py

app.py

@@ -1,101 +1,103 @@
-import torch
-import re
-import gradio as gr
-import soundfile as sf
-import numpy as np
-from transformers import SpeechT5HifiGan
-from IPython.display import Audio
-from transformers import SpeechT5ForTextToSpeech
-from transformers import SpeechT5Processor
-
-# helper function
-number_words = {
-    0: "zero", 1: "one", 2: "two", 3: "three", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight", 9: "nine",
-    10: "ten", 11: "eleven", 12: "twelve", 13: "thirteen", 14: "fourteen", 15: "fifteen", 16: "sixteen", 17: "seventeen",
-    18: "eighteen", 19: "nineteen", 20: "twenty", 30: "thirty", 40: "forty", 50: "fifty", 60: "sixty", 70: "seventy",
-    80: "eighty", 90: "ninety", 100: "hundred", 1000: "thousand"
-}
-replacements = [
-    ("“", '"'),
-    ("”", '"'),
-    ("’", ","),
-    ("_", " "),
-    ("\xa0", " "),
-    ("\n", " "),
-    ("$","dollar"),
-    ("%","percent"),
-    ("&","and"),
-    ("*","star"),
-    ("+","plus"),
-    ("—","-")
-
-]
-def number_to_words(number):
-    if number < 20:
-        return number_words[number]
-    elif number < 100:
-        tens, unit = divmod(number, 10)
-        return number_words[tens * 10] + (" " + number_words[unit] if unit else "")
-    elif number < 1000:
-        hundreds, remainder = divmod(number, 100)
-        return (number_words[hundreds] + " hundred" if hundreds > 1 else "hundred") + (" " + number_to_words(remainder) if remainder else "")
-    elif number < 1000000:
-        thousands, remainder = divmod(number, 1000)
-        return (number_to_words(thousands) + " thousand" if thousands > 1 else "thousand") + (" " + number_to_words(remainder) if remainder else "")
-    elif number < 1000000000:
-        millions, remainder = divmod(number, 1000000)
-        return number_to_words(millions) + " million" + (" " + number_to_words(remainder) if remainder else "")
-    elif number < 1000000000000:
-        billions, remainder = divmod(number, 1000000000)
-        return number_to_words(billions) + " billion" + (" " + number_to_words(remainder) if remainder else "")
-    else:
-        return str(number)
-
-def replace_numbers_with_words(text):
-
-    def replace(match):
-        number = int(match.group())
-        return number_to_words(number)
-
-    # Find the numbers and change with words.
-    result = re.sub(r'\b\d+\b', replace, text)
-
-    return result
-
-def cleanup_text(text):
-    for src, dst in replacements:
-        text = text.replace(src, dst)
-    return text
-
-
-model = SpeechT5ForTextToSpeech.from_pretrained(
-    "Yassmen/speecht5_finetuned_english_tehnical"
-)
-
-checkpoint = "microsoft/speecht5_tts"
-processor = SpeechT5Processor.from_pretrained(checkpoint)
-
-
-
-def generate_wav_file(text):
-
-  converted_text = replace_numbers_with_words(text)
-  cleaned_text = cleanup_text(converted_text)
-  final_text = normalize_text(cleaned_text)
-  inputs = processor(text=final_text, return_tensors="pt")
-  speaker_embeddings = torch.tensor(np.load('speaker_embedding.npy'))
-  vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-  speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
-
-
-  return Audio(speech.numpy(), rate=16000)
-
-iface = gr.Interface(
-    fn=generate_wav_file,
-    inputs=gr.Textbox(lines=3, label="Enter text to convert to speech"),
-    outputs="audio",
-    title="Text-to-Speech Technical EN"
-)
-if __name__ == "__main__":
-  iface.launch()
-
+import torch
+import re
+import gradio as gr
+import soundfile as sf
+import numpy as np
+from transformers import SpeechT5HifiGan
+from IPython.display import Audio
+from transformers import SpeechT5ForTextToSpeech
+from transformers import SpeechT5Processor
+
+# helper function
+number_words = {
+    0: "zero", 1: "one", 2: "two", 3: "three", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight", 9: "nine",
+    10: "ten", 11: "eleven", 12: "twelve", 13: "thirteen", 14: "fourteen", 15: "fifteen", 16: "sixteen", 17: "seventeen",
+    18: "eighteen", 19: "nineteen", 20: "twenty", 30: "thirty", 40: "forty", 50: "fifty", 60: "sixty", 70: "seventy",
+    80: "eighty", 90: "ninety", 100: "hundred", 1000: "thousand"
+}
+replacements = [
+    ("“", '"'),
+    ("”", '"'),
+    ("’", ","),
+    ("_", " "),
+    ("\xa0", " "),
+    ("\n", " "),
+    ("$","dollar"),
+    ("%","percent"),
+    ("&","and"),
+    ("*","star"),
+    ("+","plus"),
+    ("—","-")
+
+]
+def number_to_words(number):
+    if number < 20:
+        return number_words[number]
+    elif number < 100:
+        tens, unit = divmod(number, 10)
+        return number_words[tens * 10] + (" " + number_words[unit] if unit else "")
+    elif number < 1000:
+        hundreds, remainder = divmod(number, 100)
+        return (number_words[hundreds] + " hundred" if hundreds > 1 else "hundred") + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000:
+        thousands, remainder = divmod(number, 1000)
+        return (number_to_words(thousands) + " thousand" if thousands > 1 else "thousand") + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000000:
+        millions, remainder = divmod(number, 1000000)
+        return number_to_words(millions) + " million" + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000000000:
+        billions, remainder = divmod(number, 1000000000)
+        return number_to_words(billions) + " billion" + (" " + number_to_words(remainder) if remainder else "")
+    else:
+        return str(number)
+
+def replace_numbers_with_words(text):
+
+    def replace(match):
+        number = int(match.group())
+        return number_to_words(number)
+
+    # Find the numbers and change with words.
+    result = re.sub(r'\b\d+\b', replace, text)
+
+    return result
+
+def cleanup_text(text):
+    for src, dst in replacements:
+        text = text.replace(src, dst)
+    return text
+
+
+model = SpeechT5ForTextToSpeech.from_pretrained(
+    "Yassmen/speecht5_finetuned_english_tehnical"
+)
+
+checkpoint = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(checkpoint)
+
+
+
+def generate_wav_file(text):
+  try:  
+      converted_text = replace_numbers_with_words(text)
+      cleaned_text = cleanup_text(converted_text)
+      final_text = normalize_text(cleaned_text)
+      inputs = processor(text=final_text, return_tensors="pt")
+      speaker_embeddings = torch.tensor(np.load('speaker_embedding.npy'))
+      vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+      speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+      return Audio(speech.numpy(), rate=16000)
+  except Exception as e:
+        print(f"Error: {e}")
+        return None 
+      
+
+iface = gr.Interface(
+    fn=generate_wav_file,
+    inputs=gr.Textbox(lines=3, label="Enter text to convert to speech"),
+    outputs="audio",
+    title="Text-to-Speech Technical EN"
+)
+if __name__ == "__main__":
+  iface.launch()
+