Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from snac import SNAC
+import soundfile as sf
+
+# Model configuration for 4-bit inference
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "akh99/veena-hinglish",
+    quantization_config=quantization_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("akh99/veena-hinglish", trust_remote_code=True)
+
+# Initialize SNAC decoder
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()
+
+# Control token IDs (fixed for Veena)
+START_OF_SPEECH_TOKEN = 128257
+END_OF_SPEECH_TOKEN = 128258
+START_OF_HUMAN_TOKEN = 128259
+END_OF_HUMAN_TOKEN = 128260
+START_OF_AI_TOKEN = 128261
+END_OF_AI_TOKEN = 128262
+AUDIO_CODE_BASE_OFFSET = 128266
+
+# Available speakers
+speakers = ["kavya", "agastya", "maitri", "vinaya"]
+
+def generate_speech(text, speaker="kavya", temperature=0.01, top_p=0.3):
+    """Generate speech from text using specified speaker voice"""
+
+    # Prepare input with speaker token
+    prompt = f"<spk_{speaker}> {text}"
+    prompt_tokens = tokenizer.encode(prompt, add_special_tokens=False)
+
+    # Construct full sequence: [HUMAN] <spk_speaker> text [/HUMAN] [AI] [SPEECH]
+    input_tokens = [
+        START_OF_HUMAN_TOKEN,
+        *prompt_tokens,
+        END_OF_HUMAN_TOKEN,
+        START_OF_AI_TOKEN,
+        START_OF_SPEECH_TOKEN
+    ]
+
+    input_ids = torch.tensor([input_tokens], device=model.device)
+
+    # Calculate max tokens based on text length
+    max_tokens = min(int(len(text) * 1.3) * 7 + 21, 700)
+
+    # Generate audio tokens
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            max_new_tokens=max_tokens,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=1.05,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=[END_OF_SPEECH_TOKEN, END_OF_AI_TOKEN]
+        )
+
+    # Extract SNAC tokens
+    generated_ids = output[0][len(input_tokens):].tolist()
+    snac_tokens = [
+        token_id for token_id in generated_ids
+        if AUDIO_CODE_BASE_OFFSET <= token_id < (AUDIO_CODE_BASE_OFFSET + 7 * 4096)
+    ]
+
+    if not snac_tokens:
+        raise ValueError("No audio tokens generated")
+
+    # Decode audio
+    audio = decode_snac_tokens(snac_tokens, snac_model)
+    return audio
+
+def decode_snac_tokens(snac_tokens, snac_model):
+    """De-interleave and decode SNAC tokens to audio"""
+    if not snac_tokens or len(snac_tokens) % 7 != 0:
+        return None
+
+    # Get the device of the SNAC model. Fixed by Shresth to run on colab notebook :)
+    snac_device = next(snac_model.parameters()).device
+
+    # De-interleave tokens into 3 hierarchical levels
+    codes_lvl = [[] for _ in range(3)]
+    llm_codebook_offsets = [AUDIO_CODE_BASE_OFFSET + i * 4096 for i in range(7)]
+
+    for i in range(0, len(snac_tokens), 7):
+        # Level 0: Coarse (1 token)
+        codes_lvl[0].append(snac_tokens[i] - llm_codebook_offsets[0])
+        # Level 1: Medium (2 tokens)
+        codes_lvl[1].append(snac_tokens[i+1] - llm_codebook_offsets[1])
+        codes_lvl[1].append(snac_tokens[i+4] - llm_codebook_offsets[4])
+        # Level 2: Fine (4 tokens)
+        codes_lvl[2].append(snac_tokens[i+2] - llm_codebook_offsets[2])
+        codes_lvl[2].append(snac_tokens[i+3] - llm_codebook_offsets[3])
+        codes_lvl[2].append(snac_tokens[i+5] - llm_codebook_offsets[5])
+        codes_lvl[2].append(snac_tokens[i+6] - llm_codebook_offsets[6])
+
+    # Convert to tensors for SNAC decoder
+    hierarchical_codes = []
+    for lvl_codes in codes_lvl:
+        tensor = torch.tensor(lvl_codes, dtype=torch.int32, device=snac_device).unsqueeze(0)
+        if torch.any((tensor < 0) | (tensor > 4095)):
+            raise ValueError("Invalid SNAC token values")
+        hierarchical_codes.append(tensor)
+
+    # Decode with SNAC
+    with torch.no_grad():
+        audio_hat = snac_model.decode(hierarchical_codes)
+
+    return audio_hat.squeeze().clamp(-1, 1).cpu().numpy()
+
+# --- Example Usage ---
+# Hinglish
+'''
+text_mixed = "Laptop switch on nahi ho raha shayad charging problem hai."
+audio = generate_speech(text_mixed, speaker="kavya")
+sf.write("output_hinglish.wav", audio, 24000)
+'''
+
+def interface_fn(text, speaker):
+    audio = generate_speech(text, speaker=speaker)
+    # Save to a temporary file for Gradio to play
+    output_path = "output.wav"
+    sf.write(output_path, audio, 24000)
+    return output_path
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=interface_fn,
+    inputs=[gr.Textbox(label="Text"), gr.Dropdown(choices=speakers, label="Speaker")],
+    outputs=gr.Audio(label="Generated Audio")
+)
+
+demo.launch()