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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from parler_tts import ParlerTTSForConditionalGeneration
import soundfile as sf
# Load Ganga-2-1B from local directory
@st.cache(allow_output_mutation=True)
def load_ganga_model():
tokenizer_2 = AutoTokenizer.from_pretrained("Ganga_model")
model_2 = AutoModelForCausalLM.from_pretrained("Ganga_model")
return tokenizer_2, model_2
# Load Indic-TTS from local directory
@st.cache(allow_output_mutation=True)
def load_indic_tts():
tts_model = ParlerTTSForConditionalGeneration.from_pretrained("Indic_TTS_model")
tts_tokenizer = AutoTokenizer.from_pretrained("Indic_TTS_model")
description_tokenizer = AutoTokenizer.from_pretrained("Indic_TTS_model")
return tts_model, tts_tokenizer, description_tokenizer
# Main app
def main():
st.title("Hindi Text-to-Speech using Ganga and Indic-TTS")
st.text("Enter Hindi text, generate additional text using Ganga, and convert it to speech.")
# User Input (Empty by default)
input_text = st.text_area("Enter Hindi Text", "")
if st.button("Generate Speech"):
if not input_text.strip():
st.error("Please enter some text before generating speech.")
return
with st.spinner("Generating Text with Ganga..."):
# Ganga-2-1B Inference
tokenizer_2, model_2 = load_ganga_model()
input_ids = tokenizer_2.encode(input_text, return_tensors="pt").to("cpu") # Ensure CPU usage
outputs = model_2.generate(input_ids, max_new_tokens=100, do_sample=True, top_k=10, top_p=0.95, temperature=0.7)
final_output = tokenizer_2.decode(outputs[0])
st.success(f"Generated Text: {final_output}")
with st.spinner("Synthesizing Speech with Indic-TTS..."):
# Indic-TTS Inference
model, tokenizer, description_tokenizer = load_indic_tts()
description = "Divya's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise."
description_input_ids = description_tokenizer(description, return_tensors="pt").to("cpu")
prompt_input_ids = tokenizer(final_output, return_tensors="pt").to("cpu")
generation = model.generate(input_ids=description_input_ids.input_ids,
attention_mask=description_input_ids.attention_mask,
prompt_input_ids=prompt_input_ids.input_ids,
prompt_attention_mask=prompt_input_ids.attention_mask)
audio_arr = generation.cpu().numpy().squeeze()
sf.write("output_speech.wav", audio_arr, model.config.sampling_rate)
# Displaying the audio output
st.audio("output_speech.wav", format="audio/wav")
if __name__ == "__main__":
main()
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