Create app.py

app.py

@@ -0,0 +1,146 @@
+
+import gradio as gr
+import torch
+import soundfile as sf
+import spaces
+import os
+import numpy as np
+import re
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from speechbrain.pretrained import EncoderClassifier
+from datasets import load_dataset
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+def load_models_and_data():
+    model_name = "microsoft/speecht5_tts"
+    processor = SpeechT5Processor.from_pretrained(model_name)
+    model = SpeechT5ForTextToSpeech.from_pretrained("Aumkeshchy2003/speecht5_finetuned_AumkeshChy_italian_tts").to(device)
+    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
+    
+    spk_model_name = "speechbrain/spkrec-xvect-voxceleb"
+    speaker_model = EncoderClassifier.from_hparams(
+        source=spk_model_name,
+        run_opts={"device": device},
+        savedir=os.path.join("/tmp", spk_model_name),
+    )
+    
+    # Load a sample from a dataset for default embedding
+    dataset = load_dataset("freds0/cml_tts_dataset_italian", split="train")
+    example = dataset[14]
+    
+    return model, processor, vocoder, speaker_model, example
+
+model, processor, vocoder, speaker_model, default_example = load_models_and_data()
+
+def create_speaker_embedding(waveform):
+    with torch.no_grad():
+        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform).unsqueeze(0).to(device))
+        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
+        speaker_embeddings = speaker_embeddings.squeeze()
+    return speaker_embeddings
+
+def prepare_default_embedding(example):
+    audio = example["audio"]
+    return create_speaker_embedding(audio["array"])
+
+default_embedding = prepare_default_embedding(default_example)
+
+replacements = [
+    ('à', 'ah'),
+    ('è', 'eh'),
+    ('ì', 'ee'),
+    ('í', 'ee'),
+    ('ï', 'ee'),
+    ('ò', 'aw'),
+    ('ó', 'oh'),
+    ('ù', 'oo'),
+    ('ú', 'oo')
+]
+
+number_words = {
+     0: "zero", 1: "oo-noh", 2: "doo-eh", 3: "tre", 4: "quattro", 5: "chinque", 6: "sei", 7: "sette", 8: "otto", 9: "nove",
+    10: "decei", 11: "undici", 12: "dodici", 13: "tredici", 14: "quattordici", 15: "quindici", 16: "sedici", 17: "diciassette",
+    18: "diciotto", 19: "diciannove", 20: "venti", 30: "trenta", 40: "quaranta", 50: "cinquanta", 60: "sessanta", 70: "settanta",
+    80: "ottanta", 90: "novanta", 100: "cento", 1000: "mille"
+}
+
+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] + " centi" if hundreds > 1 else " centi") + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000:
+        thousands, remainder = divmod(number, 1000)
+        return (number_to_words(thousands) + " mille" if thousands > 1 else " mille") + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000000:
+        millions, remainder = divmod(number, 1000000)
+        return number_to_words(millions) + " millione" + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000000000:
+        billions, remainder = divmod(number, 1000000000)
+        return number_to_words(billions) + " milliardo" + (" " + 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 normalize_text(text):
+    # Convert to lowercase
+    text = text.lower()
+    
+    # Replace numbers with words
+    text = replace_numbers_with_words(text)
+    
+    # Apply character replacements
+    for old, new in replacements:
+        text = text.replace(old, new)
+    
+    # Remove punctuation
+    text = re.sub(r'[^\w\s]', '', text)
+    
+    return text
+
+@spaces.GPU(duration=60)
+def text_to_speech(text, audio_file=None):
+    # Normalize the input text
+    normalized_text = normalize_text(text)
+    
+    # Prepare the input for the model
+    inputs = processor(text=normalized_text, return_tensors="pt").to(device)
+    
+    # Use the default speaker embedding
+    speaker_embeddings = default_embedding
+    
+    # Generate speech
+    with torch.no_grad():
+        speech = model.generate_speech(inputs["input_ids"], speaker_embeddings.unsqueeze(0), vocoder=vocoder)
+    
+    speech_np = speech.cpu().numpy()
+
+    return (24000, speech_np)
+    
+iface = gr.Interface(
+    fn=text_to_speech,
+    inputs=[
+        gr.Textbox(label="Enter Italian text to convert to speech")
+    ],
+    outputs=[
+        gr.Audio(label="Generated Speech", type="numpy")
+    ],
+    title="Italian SpeechT5 Text-to-Speech Demo",
+    description="Enter Italian text, and listen to the generated speech."
+)
+
+iface.launch(share=True)