Create app.py

app.py

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+import spaces
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+import soundfile as sf
+from xcodec2.modeling_xcodec2 import XCodec2Model
+import torchaudio
+import gradio as gr
+import tempfile
+
+llasa_3b ='HKUSTAudio/Llasa-8B'
+
+tokenizer = AutoTokenizer.from_pretrained(llasa_3b)
+
+model = AutoModelForCausalLM.from_pretrained(
+    llasa_3b,
+    trust_remote_code=True,
+    device_map='cuda',
+)
+
+model_path = "srinivasbilla/xcodec2"
+ 
+Codec_model = XCodec2Model.from_pretrained(model_path)
+Codec_model.eval().cuda()
+
+whisper_turbo_pipe = pipeline(
+    "automatic-speech-recognition",
+    model="openai/whisper-large-v3-turbo",
+    torch_dtype=torch.float16,
+    device='cuda',
+)
+
+def ids_to_speech_tokens(speech_ids):
+ 
+    speech_tokens_str = []
+    for speech_id in speech_ids:
+        speech_tokens_str.append(f"<|s_{speech_id}|>")
+    return speech_tokens_str
+
+def extract_speech_ids(speech_tokens_str):
+ 
+    speech_ids = []
+    for token_str in speech_tokens_str:
+        if token_str.startswith('<|s_') and token_str.endswith('|>'):
+            num_str = token_str[4:-2]
+
+            num = int(num_str)
+            speech_ids.append(num)
+        else:
+            print(f"Unexpected token: {token_str}")
+    return speech_ids
+
+@spaces.GPU(duration=60)
+def infer(sample_audio_path, target_text, progress=gr.Progress()):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        progress(0, 'Loading and trimming audio...')
+        waveform, sample_rate = torchaudio.load(sample_audio_path)
+        if len(waveform[0])/sample_rate > 15:
+            gr.Warning("Trimming audio to first 15secs.")
+            waveform = waveform[:, :sample_rate*15]
+
+        # Check if the audio is stereo (i.e., has more than one channel)
+        if waveform.size(0) > 1:
+            # Convert stereo to mono by averaging the channels
+            waveform_mono = torch.mean(waveform, dim=0, keepdim=True)
+        else:
+            # If already mono, just use the original waveform
+            waveform_mono = waveform
+
+        prompt_wav = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform_mono)
+        prompt_text = whisper_turbo_pipe(prompt_wav[0].numpy())['text'].strip()
+        progress(0.5, 'Transcribed! Generating speech...')
+
+        if len(target_text) == 0:
+            return None
+        elif len(target_text) > 300:
+            gr.Warning("Text is too long. Please keep it under 300 characters.")
+            target_text = target_text[:300]
+            
+        input_text = prompt_text + ' ' + target_text
+
+        #TTS start!
+        with torch.no_grad():
+            # Encode the prompt wav
+            vq_code_prompt = Codec_model.encode_code(input_waveform=prompt_wav)
+
+            vq_code_prompt = vq_code_prompt[0,0,:]
+            # Convert int 12345 to token <|s_12345|>
+            speech_ids_prefix = ids_to_speech_tokens(vq_code_prompt)
+
+            formatted_text = f"<|TEXT_UNDERSTANDING_START|>{input_text}<|TEXT_UNDERSTANDING_END|>"
+
+            # Tokenize the text and the speech prefix
+            chat = [
+                {"role": "user", "content": "Convert the text to speech:" + formatted_text},
+                {"role": "assistant", "content": "<|SPEECH_GENERATION_START|>" + ''.join(speech_ids_prefix)}
+            ]
+
+            input_ids = tokenizer.apply_chat_template(
+                chat, 
+                tokenize=True, 
+                return_tensors='pt', 
+                continue_final_message=True
+            )
+            input_ids = input_ids.to('cuda')
+            speech_end_id = tokenizer.convert_tokens_to_ids('<|SPEECH_GENERATION_END|>')
+
+            # Generate the speech autoregressively
+            outputs = model.generate(
+                input_ids,
+                max_length=2048,  # We trained our model with a max length of 2048
+                eos_token_id= speech_end_id ,
+                do_sample=True,
+                top_p=1,           
+                temperature=0.8
+            )
+            # Extract the speech tokens
+            generated_ids = outputs[0][input_ids.shape[1]-len(speech_ids_prefix):-1]
+
+            speech_tokens = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)   
+
+            # Convert  token <|s_23456|> to int 23456 
+            speech_tokens = extract_speech_ids(speech_tokens)
+
+            speech_tokens = torch.tensor(speech_tokens).cuda().unsqueeze(0).unsqueeze(0)
+
+            # Decode the speech tokens to speech waveform
+            gen_wav = Codec_model.decode_code(speech_tokens) 
+
+            # if only need the generated part
+            gen_wav = gen_wav[:,:,prompt_wav.shape[1]:]
+
+            progress(1, 'Synthesized!')
+
+        return (16000, gen_wav[0, 0, :].cpu().numpy())
+
+with gr.Blocks() as app_tts:
+    gr.Markdown("# Zero Shot Voice Clone TTS")
+    ref_audio_input = gr.Audio(label="Reference Audio", type="filepath")
+    gen_text_input = gr.Textbox(label="Text to Generate", lines=10)
+
+    generate_btn = gr.Button("Synthesize", variant="primary")
+
+    audio_output = gr.Audio(label="Synthesized Audio")
+
+    generate_btn.click(
+        infer,
+        inputs=[
+            ref_audio_input,
+            gen_text_input,
+        ],
+        outputs=[audio_output],
+    )
+
+with gr.Blocks() as app_credits:
+    gr.Markdown("""
+# Credits
+* [zhenye234](https://github.com/zhenye234) for the original [repo](https://github.com/zhenye234/LLaSA_training)
+* [mrfakename](https://huggingface.co/mrfakename) for the [gradio demo code](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)        
+""")
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        """
+# llasa 3b TTS
+This is a local web UI for llasa 3b SOTA(imo) Zero Shot Voice Cloning and TTS model.
+The checkpoints support English and Chinese.
+If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt.
+"""
+    )
+    gr.TabbedInterface([app_tts], ["TTS"])
+
+
+app.launch()