Create app.py

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+import torch
+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/SpeechT5-fine-tune-en")
+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("Yassmen/TTS_English_Technical_data", split="train", 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 = [
+    ("0", "zero"),
+    ("1", "one"),
+    ("2", "two"),
+    ("3", "three"),
+    ("4", "four"),
+    ("5", "five"),
+    ("6", "six"),
+    ("7", "seven"),
+    ("8", "eight"),
+    ("9", "nine"),
+    ("_", " ")
+    ]
+    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="Technical english Text-to-Speech",
+    description="Enter english text to convert to speech"
+)
+
+iface.launch()