testing spaces

app.py

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+import spaces
+from snac import SNAC
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import snapshot_download
+from dotenv import load_dotenv
+load_dotenv()
+
+# Check if CUDA is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+print("Loading SNAC model...")
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+snac_model = snac_model.to(device)
+
+model_name = "MrDragonFox/mOrpheus_3B-1Base_early_preview-v1-25000"
+
+# Download only model config and safetensors
+snapshot_download(
+    repo_id=model_name,
+    allow_patterns=[
+        "config.json",
+        "*.safetensors",
+        "model.safetensors.index.json",
+    ],
+    ignore_patterns=[
+        "optimizer.pt",
+        "pytorch_model.bin",
+        "training_args.bin",
+        "scheduler.pt",
+        "tokenizer.json",
+        "tokenizer_config.json",
+        "special_tokens_map.json",
+        "vocab.json",
+        "merges.txt",
+        "tokenizer.*"
+    ]
+)
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
+model.to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+print(f"Orpheus model loaded to {device}")
+
+# Process text prompt
+def process_prompt(prompt, voice, tokenizer, device):
+    prompt = f"{voice}: {prompt}"
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    
+    start_token = torch.tensor([[128259]], dtype=torch.int64)  # Start of human
+    end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64)  # End of text, End of human
+    
+    modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1)  # SOH SOT Text EOT EOH
+    
+    # No padding needed for single input
+    attention_mask = torch.ones_like(modified_input_ids)
+    
+    return modified_input_ids.to(device), attention_mask.to(device)
+
+# Parse output tokens to audio
+def parse_output(generated_ids):
+    token_to_find = 128257
+    token_to_remove = 128258
+    
+    token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
+
+    if len(token_indices[1]) > 0:
+        last_occurrence_idx = token_indices[1][-1].item()
+        cropped_tensor = generated_ids[:, last_occurrence_idx+1:]
+    else:
+        cropped_tensor = generated_ids
+
+    processed_rows = []
+    for row in cropped_tensor:
+        masked_row = row[row != token_to_remove]
+        processed_rows.append(masked_row)
+
+    code_lists = []
+    for row in processed_rows:
+        row_length = row.size(0)
+        new_length = (row_length // 7) * 7
+        trimmed_row = row[:new_length]
+        trimmed_row = [t - 128266 for t in trimmed_row]
+        code_lists.append(trimmed_row)
+        
+    return code_lists[0]  # Return just the first one for single sample
+
+# Redistribute codes for audio generation
+def redistribute_codes(code_list, snac_model):
+    device = next(snac_model.parameters()).device  # Get the device of SNAC model
+    
+    layer_1 = []
+    layer_2 = []
+    layer_3 = []
+    for i in range((len(code_list)+1)//7):
+        layer_1.append(code_list[7*i])
+        layer_2.append(code_list[7*i+1]-4096)
+        layer_3.append(code_list[7*i+2]-(2*4096))
+        layer_3.append(code_list[7*i+3]-(3*4096))
+        layer_2.append(code_list[7*i+4]-(4*4096))
+        layer_3.append(code_list[7*i+5]-(5*4096))
+        layer_3.append(code_list[7*i+6]-(6*4096))
+        
+    # Move tensors to the same device as the SNAC model
+    codes = [
+        torch.tensor(layer_1, device=device).unsqueeze(0),
+        torch.tensor(layer_2, device=device).unsqueeze(0),
+        torch.tensor(layer_3, device=device).unsqueeze(0)
+    ]
+    
+    audio_hat = snac_model.decode(codes)
+    return audio_hat.detach().squeeze().cpu().numpy()  # Always return CPU numpy array
+
+# Main generation function
+@spaces.GPU()
+def generate_speech(text, voice, temperature, top_p, repetition_penalty, max_new_tokens, progress=gr.Progress()):
+    if not text.strip():
+        return None
+    
+    try:
+        progress(0.1, "Processing text...")
+        input_ids, attention_mask = process_prompt(text, voice, tokenizer, device)
+        
+        progress(0.3, "Generating speech tokens...")
+        with torch.no_grad():
+            generated_ids = model.generate(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                max_new_tokens=max_new_tokens,
+                do_sample=True,
+                temperature=temperature,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                num_return_sequences=1,
+                eos_token_id=128258,
+            )
+        
+        progress(0.6, "Processing speech tokens...")
+        code_list = parse_output(generated_ids)
+        
+        progress(0.8, "Converting to audio...")
+        audio_samples = redistribute_codes(code_list, snac_model)
+        
+        return (24000, audio_samples)  # Return sample rate and audio
+    except Exception as e:
+        print(f"Error generating speech: {e}")
+        return None
+
+# Examples for the UI
+examples = [
+    ["Hey there my name is Baddy, <chuckle> and I'm a speech generation model that can sound like a person.", "baddy", 0.6, 0.95, 1.1, 1200],
+    ["I've also been taught to understand and produce paralinguistic things <sigh> like sighing, or <laugh> laughing, or <yawn> yawning!", "baddy", 0.7, 0.95, 1.1, 1200],
+    ["I live in San Francisco, and have, uhm let's see, 3 billion 7 hundred ... <gasp> well, lets just say a lot of parameters.", "baddy", 0.6, 0.9, 1.2, 1200],
+    ["Sometimes when I talk too much, I need to <cough> excuse myself. <sniffle> The weather has been quite cold lately.", "baddy", 0.65, 0.9, 1.1, 1200],
+    ["Public speaking can be challenging. <groan> But with enough practice, anyone can become better at it.", "baddy", 0.7, 0.95, 1.1, 1200],
+    ["The hike was exhausting but the view from the top was absolutely breathtaking! <sigh> It was totally worth it.", "baddy", 0.65, 0.9, 1.15, 1200],
+    ["Did you hear that joke? <laugh> I couldn't stop laughing when I first heard it. <chuckle> It's still funny.", "baddy", 0.7, 0.95, 1.1, 1200],
+    ["After running the marathon, I was so tired <yawn> and needed a long rest. <sigh> But I felt accomplished.", "baddy", 0.6, 0.95, 1.1, 1200]
+]
+
+# Available voices
+VOICES = ["baddy"]
+
+# Available Emotive Tags
+EMOTIVE_TAGS = ["`<laugh>`", "`<chuckle>`", "`<sigh>`", "`<cough>`", "`<sniffle>`", "`<groan>`", "`<yawn>`", "`<gasp>`"]
+
+# Create Gradio interface
+with gr.Blocks(title="Morpheus Text-to-Speech - uncensored orpheus") as demo:
+    gr.Markdown(f"""
+    # 🎵 Morpheus Text-to-Speech
+    Enter your text below and hear it converted to natural-sounding speech with the Morpheus TTS model.
+    
+    ## Tips for better prompts:
+    - Add paralinguistic elements like {", ".join(EMOTIVE_TAGS)} or `uhm` for more human-like speech.
+    - Longer text prompts generally work better than very short phrases
+    - Increasing `repetition_penalty` and `temperature` makes the model speak faster.
+    """)    
+    with gr.Row():
+        with gr.Column(scale=3):
+            text_input = gr.Textbox(
+                label="Text to speak", 
+                placeholder="Enter your text here...",
+                lines=5
+            )
+            voice = gr.Dropdown(
+                choices=VOICES, 
+                value="baddy", 
+                label="Voice"
+            )
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                temperature = gr.Slider(
+                    minimum=0.1, maximum=1.5, value=0.6, step=0.05,
+                    label="Temperature", 
+                    info="Higher values (0.7-1.0) create more expressive but less stable speech"
+                )
+                top_p = gr.Slider(
+                    minimum=0.1, maximum=1.0, value=0.95, step=0.05,
+                    label="Top P", 
+                    info="Nucleus sampling threshold"
+                )
+                repetition_penalty = gr.Slider(
+                    minimum=1.0, maximum=2.0, value=1.1, step=0.05,
+                    label="Repetition Penalty", 
+                    info="Higher values discourage repetitive patterns"
+                )
+                max_new_tokens = gr.Slider(
+                    minimum=100, maximum=2000, value=1200, step=100,
+                    label="Max Length", 
+                    info="Maximum length of generated audio (in tokens)"
+                )
+            
+            with gr.Row():
+                submit_btn = gr.Button("Generate Speech", variant="primary")
+                clear_btn = gr.Button("Clear")
+                
+        with gr.Column(scale=2):
+            audio_output = gr.Audio(label="Generated Speech", type="numpy")
+            
+    # Set up examples
+    gr.Examples(
+        examples=examples,
+        inputs=[text_input, voice, temperature, top_p, repetition_penalty, max_new_tokens],
+        outputs=audio_output,
+        fn=generate_speech,
+        cache_examples=True,
+    )
+    
+    # Set up event handlers
+    submit_btn.click(
+        fn=generate_speech,
+        inputs=[text_input, voice, temperature, top_p, repetition_penalty, max_new_tokens],
+        outputs=audio_output
+    )
+    
+    clear_btn.click(
+        fn=lambda: (None, None),
+        inputs=[],
+        outputs=[text_input, audio_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.queue().launch(share=False, ssr_mode=False)

requirements.txt

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+snac
+python-dotenv
+transformers
+torch
+spaces