Update app.py

app.py

@@ -1,64 +1,37 @@
 import gradio as gr
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from transformers import AutoProcessor, AutoModelForTextToSpectrogram
+from datasets import load_dataset
 import torch
+import soundfile as sf
 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("ayush2607/speecht5_tts_technical_data")
+# Load models and processors
+processor = AutoProcessor.from_pretrained("ayush2607/speecht5_tts_technical_data")
+model = AutoModelForTextToSpectrogram.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
-try:
-    dataset = load_dataset("Yassmen/TTS_English_Technical_data", split="train")
-    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
+# Load xvector containing speaker's voice characteristics from a dataset
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
 
 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())
-
+    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+    
+    output_path = "output.wav"
+    sf.write(output_path, speech.numpy(), samplerate=16000)
+    
+    return output_path
+
+# Create Gradio interface
 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."
+    inputs=gr.Textbox(label="Enter text to convert to speech"),
+    outputs=gr.Audio(label="Generated Speech"),
+    title="Text-to-Speech Converter",
+    description="Convert text to speech using the SpeechT5 model."
 )
 
+# Launch the app
 iface.launch()