Update app.py

app.py

@@ -1,90 +1,129 @@
+# import streamlit as st
+# from transformers import SeamlessM4Tv2Model, AutoProcessor
+# import torch
+# import numpy as np
+# from scipy.io.wavfile import write
+# import re
+# from io import BytesIO
+
+# # Load the processor and model
+# processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
+# model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
+
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# model.to(device)
+
+# # Number to words function for Uzbek
+# number_words = {
+#     0: "nol", 1: "bir", 2: "ikki", 3: "uch", 4: "to'rt", 5: "besh", 6: "olti", 7: "yetti", 8: "sakkiz", 9: "to'qqiz",
+#     10: "o'n", 11: "o'n bir", 12: "o'n ikki", 13: "o'n uch", 14: "o'n to'rt", 15: "o'n besh", 16: "o'n oltı", 17: "o'n yetti",
+#     18: "o'n sakkiz", 19: "o'n toqqiz", 20: "yigirma", 30: "o'ttiz", 40: "qirq", 50: "ellik", 60: "oltmish", 70: "yetmish",
+#     80: "sakson", 90: "to'qson", 100: "yuz", 1000: "ming", 1000000: "million"
+# }
+
+# 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] + " yuz" if hundreds > 1 else "yuz") + (" " + number_to_words(remainder) if remainder else "")
+#     elif number < 1000000:
+#         thousands, remainder = divmod(number, 1000)
+#         return (number_to_words(thousands) + " ming" if thousands > 1 else "ming") + (" " + 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) + " milliard" + (" " + 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)
+#     result = re.sub(r'\b\d+\b', replace, text)
+#     return result
+
+# # Replacements
+# replacements = [
+#     ("bo‘ladi", "bo'ladi"),
+#     ("yog‘ingarchilik", "yog'ingarchilik"),
+# ]
+
+# def cleanup_text(text):
+#     for src, dst in replacements:
+#         text = text.replace(src, dst)
+#     return text
+
+# # Streamlit App
+# st.title("Text-to-Speech using Seamless M4T Model")
+
+# # User Input
+# user_input = st.text_area("Enter the text for speech generation", height=200)
+
+# # Process the text and generate speech
+# if st.button("Generate Speech"):
+#     if user_input.strip():
+#         # Apply text transformations
+#         converted_text = replace_numbers_with_words(user_input)
+#         cleaned_text = cleanup_text(converted_text)
+        
+#         # Process input for model
+#         inputs = processor(text=cleaned_text, src_lang="uzn", return_tensors="pt").to(device)
+        
+#         # Generate audio from text
+#         audio_array_from_text = model.generate(**inputs, tgt_lang="uzn")[0].cpu().numpy().squeeze()
+
+#         # Save to BytesIO
+#         audio_io = BytesIO()
+#         write(audio_io, 16000, audio_array_from_text.astype(np.float32))
+#         audio_io.seek(0)
+
+#         # Provide audio for playback
+#         st.audio(audio_io, format='audio/wav')
+#     else:
+#         st.warning("Please enter some text to generate speech.")
 import streamlit as st
-from transformers import SeamlessM4Tv2Model, AutoProcessor
+from transformers import SeamlessM4TTokenizer, SeamlessM4Tv2Model
 import torch
 import numpy as np
 from scipy.io.wavfile import write
-import re
 from io import BytesIO
 
-# Load the processor and model
-processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
+# Load the tokenizer and model
+tokenizer = SeamlessM4TTokenizer.from_pretrained("facebook/seamless-m4t-v2-large")
 model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
 
+# Set the device (CUDA if available, else CPU)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-# Number to words function for Uzbek
-number_words = {
-    0: "nol", 1: "bir", 2: "ikki", 3: "uch", 4: "to'rt", 5: "besh", 6: "olti", 7: "yetti", 8: "sakkiz", 9: "to'qqiz",
-    10: "o'n", 11: "o'n bir", 12: "o'n ikki", 13: "o'n uch", 14: "o'n to'rt", 15: "o'n besh", 16: "o'n oltı", 17: "o'n yetti",
-    18: "o'n sakkiz", 19: "o'n toqqiz", 20: "yigirma", 30: "o'ttiz", 40: "qirq", 50: "ellik", 60: "oltmish", 70: "yetmish",
-    80: "sakson", 90: "to'qson", 100: "yuz", 1000: "ming", 1000000: "million"
-}
-
-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] + " yuz" if hundreds > 1 else "yuz") + (" " + number_to_words(remainder) if remainder else "")
-    elif number < 1000000:
-        thousands, remainder = divmod(number, 1000)
-        return (number_to_words(thousands) + " ming" if thousands > 1 else "ming") + (" " + 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) + " milliard" + (" " + 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)
-    result = re.sub(r'\b\d+\b', replace, text)
-    return result
-
-# Replacements
-replacements = [
-    ("bo‘ladi", "bo'ladi"),
-    ("yog‘ingarchilik", "yog'ingarchilik"),
-]
-
-def cleanup_text(text):
-    for src, dst in replacements:
-        text = text.replace(src, dst)
-    return text
-
-# Streamlit App
-st.title("Text-to-Speech using Seamless M4T Model")
-
-# User Input
-user_input = st.text_area("Enter the text for speech generation", height=200)
-
-# Process the text and generate speech
-if st.button("Generate Speech"):
-    if user_input.strip():
-        # Apply text transformations
-        converted_text = replace_numbers_with_words(user_input)
-        cleaned_text = cleanup_text(converted_text)
-        
-        # Process input for model
-        inputs = processor(text=cleaned_text, src_lang="uzn", return_tensors="pt").to(device)
-        
-        # Generate audio from text
+# Streamlit title
+st.title("Text-to-Speech with Seamless M4T Model")
+
+# Input text field
+text = st.text_area("Enter text for audio generation", "Nutq texnologiyasining til qamrovini kengaytirish...")
+
+# Button to generate audio
+if st.button("Generate Audio"):
+    if text:
+        # Preprocess the text and convert to tensor
+        inputs = tokenizer(text=text, src_lang="uzn", return_tensors="pt").to(device)
+
+        # Generate audio from the model
         audio_array_from_text = model.generate(**inputs, tgt_lang="uzn")[0].cpu().numpy().squeeze()
 
-        # Save to BytesIO
-        audio_io = BytesIO()
-        write(audio_io, 16000, audio_array_from_text.astype(np.float32))
-        audio_io.seek(0)
+        # Save the audio as a .wav file in memory
+        audio_file = BytesIO()
+        write(audio_file, 16000, audio_array_from_text.astype(np.float32))
+        audio_file.seek(0)  # Reset the pointer to the start of the file
 
-        # Provide audio for playback
-        st.audio(audio_io, format='audio/wav')
+        # Display the audio player in the Streamlit app
+        st.audio(audio_file, format="audio/wav")
     else:
-        st.warning("Please enter some text to generate speech.")
+        st.warning("Please enter text to generate audio.")