Update app.py

app.py

@@ -3,77 +3,31 @@ import librosa
 import numpy as np
 import torch
 
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
 
 
-checkpoint = "microsoft/speecht5_tts"
-processor = SpeechT5Processor.from_pretrained(checkpoint)
-model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
+# Load the model and tokenizer
+model_name = "Reverb/Mistral-7B-LoreWeaver"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-speaker_embeddings = {
-    "BDL": "Speakers/cmu_us_bdl_arctic-wav-arctic_a0009.npy",
-    "CLB": "Speakers/cmu_us_clb_arctic-wav-arctic_a0144.npy",
-    "KSP": "Speakers/cmu_us_ksp_arctic-wav-arctic_b0087.npy",
-    "RMS": "Speakers/cmu_us_rms_arctic-wav-arctic_b0353.npy",
-    "SLT": "Speakers/cmu_us_slt_arctic-wav-arctic_a0508.npy",
-}
+# Initialize the pipeline
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
 
+def generate_story(prompt):
+    # Generate a response using the model
+    responses = generator(prompt, max_length=200, num_return_sequences=1)
+    return responses[0]['generated_text']
 
-def predict(text, speaker):
-    if len(text.strip()) == 0:
-        return (16000, np.zeros(0).astype(np.int16))
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_story,
+    inputs=gr.Textbox(lines=5, placeholder="Enter your prompt here..."),
+    outputs=gr.Textbox(label="Generated Story"),
+    title="Mistral-7B-LoreWeaver Story Generator",
+    description="Enter a prompt to generate a narrative text using the Mistral-7B-LoreWeaver model."
+)
 
-    inputs = processor(text=text, return_tensors="pt")
-
-    # limit input length
-    input_ids = inputs["input_ids"]
-    input_ids = input_ids[..., :model.config.max_text_positions]
-
-    if speaker == "Surprise Me!":
-        # load one of the provided speaker embeddings at random
-        idx = np.random.randint(len(speaker_embeddings))
-        key = list(speaker_embeddings.keys())[idx]
-        speaker_embedding = np.load(speaker_embeddings[key])
-
-        # randomly shuffle the elements
-        np.random.shuffle(speaker_embedding)
-
-        # randomly flip half the values
-        x = (np.random.rand(512) >= 0.5) * 1.0
-        x[x == 0] = -1.0
-        speaker_embedding *= x
-
-        #speaker_embedding = np.random.rand(512).astype(np.float32) * 0.3 - 0.15
-    else:
-        speaker_embedding = np.load(speaker_embeddings[speaker[:3]])
-
-    speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
-
-    speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)
-
-    speech = (speech.numpy() * 32767).astype(np.int16)
-    return (16000, speech)
-
-title = "LoreWeaver: A Novel Generation Multimodal LLM"
-
-gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Text(label="Input Text"),
-        gr.Radio(label="Speaker", choices=[
-            "BDL (male)",
-            "CLB (female)",
-            "KSP (male)",
-            "RMS (male)",
-            "SLT (female)",
-            "Surprise Me!"
-        ],
-        value="BDL (male)"),
-    ],
-    outputs=[
-        gr.Audio(label="Generated Speech", type="numpy"),
-    ],
-    title=title,
-).launch()
+# Launch the interface
+iface.launch()