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import gradio as gr
import torch
#import python_multipart
import os
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset, Audio
import numpy as np
from speechbrain.inference import EncoderClassifier
# Load models and processor
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("Solo448/Speect5-common-voice-Hindi")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
# Load speaker encoder
device = "cuda" if torch.cuda.is_available() else "cpu"
speaker_model = EncoderClassifier.from_hparams(
source="speechbrain/spkrec-xvect-voxceleb",
run_opts={"device": device},
savedir=os.path.join("/tmp", "speechbrain/spkrec-xvect-voxceleb")
)
# Load a sample from the dataset for speaker embedding
try:
dataset = load_dataset("mozilla-foundation/common_voice_17_0", "hi", split="validated", trust_remote_code=True)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
sample = dataset[0]
speaker_embedding = create_speaker_embedding(sample['audio']['array'])
except Exception as e:
print(f"Error loading dataset: {e}")
# Use a random speaker embedding as fallback
speaker_embedding = torch.randn(1, 512)
def create_speaker_embedding(waveform):
with torch.no_grad():
speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
return speaker_embeddings
def text_to_speech(text):
# Clean up text
replacements = [
("अ", "a"),
("आ", "aa"),
("इ", "i"),
("ई", "ee"),
("उ", "u"),
("ऋ", "ri"),
("ए", "ae"),
("ऐ", "ai"),
("ऑ", "au"),
("ओ", "o"),
("औ", "au"),
("क", "k"),
("ख", "kh"),
("ग", "g"),
("घ", "gh"),
("च", "ch"),
("छ", "chh"),
("ज", "j"),
("झ", "jh"),
("ञ", "gna"),
("ट", "t"),
("ठ", "th"),
("ड", "d"),
("ढ", "dh"),
("ण", "nr"),
("त", "t"),
("थ", "th"),
("द", "d"),
("ध", "dh"),
("न", "n"),
("प", "p"),
("फ", "ph"),
("ब", "b"),
("भ", "bh"),
("म", "m"),
("य", "ya"),
("र", "r"),
("ल", "l"),
("व", "w"),
("श", "sha"),
("ष", "sh"),
("स", "s"),
("ह", "ha"),
("़", "ng"),
("्", ""),
("ऽ", ""),
("ा", "a"),
("ि", "i"),
("ी", "ee"),
("ु", "u"),
("ॅ", "n"),
("े", "e"),
("ै", "oi"),
("ो", "o"),
("ौ", "ou"),
("ॅ", "n"),
("ॉ", "r"),
("ू", "uh"),
("ृ", "ri"),
("ं", "n"),
("क़", "q"),
("ज़", "z"),
("ड़", "r"),
("ढ़", "rh"),
("फ़", "f"),
("|", ".")
]
for src, dst in replacements:
text = text.replace(src, dst)
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
return (16000, speech.numpy())
iface = gr.Interface(
fn=text_to_speech,
inputs="text",
outputs="audio",
title="Hindi Text-to-Speech",
description="Enter hindi text to convert to speech"
)
iface.launch(share=True) |