Create app.py

app.py

@@ -0,0 +1,120 @@
+import gradio as gr
+import torch
+import soundfile as sf
+from snac import SNAC
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def find_last_instance_of_separator(lst, element=50258):
+    reversed_list = lst[::-1]
+    try:
+        reversed_index = reversed_list.index(element)
+        return len(lst) - 1 - reversed_index
+    except ValueError:
+        raise ValueError
+
+def reconstruct_tensors(flattened_output):
+    def count_elements_between_hashes(lst):
+        try:
+            first_index = lst.index(50258)
+            second_index = lst.index(50258, first_index + 1)
+            return second_index - first_index - 1
+        except ValueError:
+            return "List does not contain two '#' symbols"
+
+    def remove_elements_before_hash(flattened_list):
+        try:
+            first_hash_index = flattened_list.index(50258)
+            return flattened_list[first_hash_index:]
+        except ValueError:
+            return "List does not contain the symbol '#'"
+
+    def list_to_torch_tensor(tensor1):
+        tensor = torch.tensor(tensor1)
+        tensor = tensor.unsqueeze(0)
+        return tensor
+
+    flattened_output = remove_elements_before_hash(flattened_output)
+    last_index = find_last_instance_of_separator(flattened_output)
+    flattened_output = flattened_output[:last_index]
+
+    codes = []
+    tensor1 = []
+    tensor2 = []
+    tensor3 = []
+    tensor4 = []
+
+    n_tensors = count_elements_between_hashes(flattened_output)
+    if n_tensors == 7:
+        for i in range(0, len(flattened_output), 8):
+            tensor1.append(flattened_output[i+1])
+            tensor2.append(flattened_output[i+2])
+            tensor3.append(flattened_output[i+3])
+            tensor3.append(flattened_output[i+4])
+            tensor2.append(flattened_output[i+5])
+            tensor3.append(flattened_output[i+6])
+            tensor3.append(flattened_output[i+7])
+            codes = [list_to_torch_tensor(tensor1).to(device), list_to_torch_tensor(tensor2).to(device), list_to_torch_tensor(tensor3).to(device)]
+
+    if n_tensors == 15:
+        for i in range(0, len(flattened_output), 16):
+            tensor1.append(flattened_output[i+1])
+            tensor2.append(flattened_output[i+2])
+            tensor3.append(flattened_output[i+3])
+            tensor4.append(flattened_output[i+4])
+            tensor4.append(flattened_output[i+5])
+            tensor3.append(flattened_output[i+6])
+            tensor4.append(flattened_output[i+7])
+            tensor4.append(flattened_output[i+8])
+            tensor2.append(flattened_output[i+9])
+            tensor3.append(flattened_output[i+10])
+            tensor4.append(flattened_output[i+11])
+            tensor4.append(flattened_output[i+12])
+            tensor3.append(flattened_output[i+13])
+            tensor4.append(flattened_output[i+14])
+            tensor4.append(flattened_output[i+15])
+            codes = [list_to_torch_tensor(tensor1).to(device), list_to_torch_tensor(tensor2).to(device), list_to_torch_tensor(tensor3).to(device), list_to_torch_tensor(tensor4).to(device)]
+
+    return codes
+
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("Lwasinam/voicera-jenny-finetune")
+    model = AutoModelForCausalLM.from_pretrained("Lwasinam/voicera-jenny-finetune").to(device)
+    snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval()
+    return model, tokenizer, snac_model
+
+def SpeechDecoder(codes, snac_model):
+    codes = codes.squeeze(0).tolist()
+    reconstructed_codes = reconstruct_tensors(codes)
+    audio_hat = snac_model.to(device).decode(reconstructed_codes)
+    audio_path = "reconstructed_audio.wav"
+    sf.write(audio_path, audio_hat.squeeze().cpu().detach().numpy(), 24000)
+    return audio_path
+
+def generate_audio(text, tokenizer, model, snac_model):
+    output_codes = []
+    with torch.no_grad():
+        input_text = text
+        input_ids = tokenizer(input_text, return_tensors='pt').to(device)
+        output_codes = model.generate(input_ids['input_ids'], attention_mask=input_ids['attention_mask'], max_length=1024,
+                                      num_beams=5, top_p=0.95, temperature=0.8, do_sample=True, repetition_penalty=2.0)
+        audio_path = SpeechDecoder(output_codes, snac_model)
+    return audio_path
+
+def main(text):
+    model, tokenizer, snac_model = load_model()
+    audio_path = generate_audio(text, tokenizer, model, snac_model)
+    return audio_path
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=main,
+    inputs=gr.inputs.Textbox(label="Enter text:", lines=2, placeholder="Type your text here..."),
+    outputs="audio",
+    title="Voicera TTS",
+    description="Generate speech from text using Voicera TTS model."
+)
+
+if __name__ == "__main__":
+    iface.launch()