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| # 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 SeamlessM4TTokenizer, SeamlessM4Tv2Model | |
| import torch | |
| import numpy as np | |
| from scipy.io.wavfile import write | |
| from io import BytesIO | |
| # Load the tokenizer and model | |
| # tokenizer = SeamlessM4TTokenizer.from_pretrained("facebook/seamless-m4t-v2-large") | |
| # model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large") | |
| # Load model directly | |
| from transformers import AutoProcessor, AutoModelForTextToSpectrogram | |
| processor = AutoProcessor.from_pretrained("Beehzod/speecht5_finetuned_uz_customData") | |
| model = AutoModelForTextToSpectrogram.from_pretrained("Beehzod/speecht5_finetuned_uz_customData") | |
| # Set the device (CUDA if available, else CPU) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| # Streamlit title | |
| st.title("Text-to-Speech with Seamless M4T Model") | |
| # Input text field | |
| text = st.text_area("Enter text for audio generation") | |
| # 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 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 | |
| # Display the audio player in the Streamlit app | |
| st.audio(audio_file, format="audio/wav") | |
| else: | |
| st.warning("Please enter text to generate audio.") | |