Create app.py

app.py

@@ -0,0 +1,149 @@
+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("fahadqazi/testts1234").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("erenfazlioglu/turkishvoicedataset", split="train")
+    example = dataset[304]
+    
+    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"])
+
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = embeddings_dataset[7306]["xvector"]
+speaker_embeddings = torch.tensor(speaker_embeddings).to(device)
+
+default_embedding = speaker_embeddings #prepare_default_embedding(default_example)
+
+# replacements = [
+#     ("â", "a"),  # Long a
+#     ("ç", "ch"),  # Ch as in "chair"
+#     ("ğ", "gh"),  # Silent g or slight elongation of the preceding vowel
+#     ("ı", "i"),   # Dotless i
+#     ("î", "i"),   # Long i
+#     ("ö", "oe"),  # Similar to German ö
+#     ("ş", "sh"),  # Sh as in "shoe"
+#     ("ü", "ue"),  # Similar to German ü
+#     ("û", "u"),   # Long u
+# ]
+
+number_words = {
+    0: "sıfır", 1: "bir", 2: "iki", 3: "üç", 4: "dört", 5: "beş", 6: "altı", 7: "yedi", 8: "sekiz", 9: "dokuz",
+    10: "on", 11: "on bir", 12: "on iki", 13: "on üç", 14: "on dört", 15: "on beş", 16: "on altı", 17: "on yedi",
+    18: "on sekiz", 19: "on dokuz", 20: "yirmi", 30: "otuz", 40: "kırk", 50: "elli", 60: "altmış", 70: "yetmiş",
+    80: "seksen", 90: "doksan", 100: "yüz", 1000: "bin"
+}
+
+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] + " yüz" if hundreds > 1 else "yüz") + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000:
+        thousands, remainder = divmod(number, 1000)
+        return (number_to_words(thousands) + " bin" if thousands > 1 else "bin") + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000000:
+        millions, remainder = divmod(number, 1000000)
+        return number_to_words(millions) + " milyon" + (" " + number_to_words(remainder) if remainder else "")
+    elif number < 1000000000000:
+        billions, remainder = divmod(number, 1000000000)
+        return number_to_words(billions) + " milyar" + (" " + 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 (16000, speech_np)
+    
+iface = gr.Interface(
+    fn=text_to_speech,
+    inputs=[
+        gr.Textbox(label="Enter Sindhi text to convert to speech")
+    ],
+    outputs=[
+        gr.Audio(label="Generated Speech", type="numpy")
+    ],
+    title="Sindhi SpeechT5 Text-to-Speech Demo",
+    description="Enter Sindhi text, and listen to the generated speech."
+)
+
+iface.launch(share=True)