Update app.py

app.py

@@ -9,131 +9,12 @@ from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5Hif
 from speechbrain.pretrained import EncoderClassifier
 from datasets import load_dataset
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# Load model directly
+from transformers import AutoProcessor, AutoModelForTextToSpectrogram
 
-def load_models_and_data():
-    model_name = "microsoft/speecht5_tts"
-    processor = SpeechT5Processor.from_pretrained(model_name)
-    model = SpeechT5ForTextToSpeech.from_pretrained("speecht5_finetuned_Aumkesh_tr").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("Yassmen/TTS_English_Technical_data", split="train")
-    example = dataset[304]
-    
-    return model, processor, vocoder, speaker_model, example
+processor = AutoProcessor.from_pretrained("Aumkeshchy2003/speecht5_finetuned_Aumkesh_tr")
+model = AutoModelForTextToSpectrogram.from_pretrained("Aumkeshchy2003/speecht5_finetuned_Aumkesh_tr")
 
-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 = [
-   ("@", "at the rate"),
-   ("$", "dollar")
-]
-
-number_words = {
-    0: "zero", 1: "one", 2: "two", 3: "tree", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight", 9: "nine",
-    10: "ten", 11: "eleven", 12: "twelve", 13: "thirteen", 14: "fourteen", 15: "fifteen", 16: "sixteen", 17: "seventeen",
-    18: "eighteen", 19: "nineteen", 20: "tweenty", 30: "thirty", 40: "forty", 50: "fifty", 60: "sixty", 70: "seventy",
-    80: "eighty", 90: "ninty", 100: "hundred", 1000: "thousand"
-}
-
-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] + " hundred" if hundreds > 1 else " hundred") + (" " + number_to_words(remainder) if remainder else "")
-    elif number < 1000000:
-        thousands, remainder = divmod(number, 1000)
-        return (number_to_words(thousands) + " thousand" if thousands > 1 else " thousand") + (" " + number_to_words(remainder) if remainder else "")
-    elif number < 1000000000:
-        millions, remainder = divmod(number, 1000000)
-        return number_to_words(millions) + " million" + (" " + number_to_words(remainder) if remainder else "")
-    elif number < 1000000000000:
-        billions, remainder = divmod(number, 1000000000)
-        return number_to_words(billions) + " billion" + (" " + 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 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=[