Create app.py

app.py

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+import gradio as gr
+import torch
+import os
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+import numpy as np
+from speechbrain.inference import EncoderClassifier
+
+# Load models and processor
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("ayush2607/speecht5_tts_technical_data")
+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
+dataset = load_dataset("Yassmen/TTS_English_Technical_data", split="train")
+dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
+sample = dataset[0]
+
+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
+
+# Create a speaker embedding from the sample
+speaker_embedding = create_speaker_embedding(sample['audio']['array'])
+
+def text_to_speech(text):
+    # Clean up text
+    replacements = [
+        ('$', 'dollar'), ('%', 'percent'), ('&', 'and'), ('*', 'asterick'),
+        ('+', 'plus'), ('1', 'one'), ('2', 'two'), ('3', 'three'), ('4', 'four'),
+        ('5', 'five'), ('6', 'six'), ('7', 'seven'), ('8', 'eight'), ('9', 'nine'),
+        ('0', 'zero'), ('@', 'at'), ('\n', ' '), ('\xa0', ' '), (',', ' '),
+        ('"', '"'), ('"', '"'),
+    ]
+    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 Text-to-Speech",
+    description="Enter technical text to convert to speech. The model has been fine-tuned on technical data."
+)
+
+iface.launch()