Create app.py

app.py

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+import gradio as gr
+from bark.generation import codec_encode, load_codec_model, generate_text_semantic
+from encodec.utils import convert_audio
+import torchaudio
+import torch
+
+model = load_codec_model(use_gpu=True)
+
+def clone_voice(audio_in, name, transcript_text):
+    # Load and pre-process the audio waveform
+    audio_filepath = audio_in # the audio WAV you want to clone (will get truncated so 5-10 seconds is probably fine, existing samples that I checked are around 7 seconds)
+    wav, sr = torchaudio.load(audio_filepath)
+    wav = convert_audio(wav, sr, model.sample_rate, model.channels)
+    wav = wav.unsqueeze(0).to('cuda')
+
+    # Extract discrete codes from EnCodec
+    with torch.no_grad():
+        encoded_frames = model.encode(wav)
+    codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()  # [n_q, T]
+
+    #"Transcription of the audio you are cloning"
+    text = transcript_text
+
+    # get seconds of audio
+    seconds = wav.shape[-1] / model.sample_rate
+
+    # generate semantic token
+    semantic_tokens = generate_text_semantic(text, max_gen_duration_s=seconds)
+
+    # move codes to cpu
+    codes = codes.cpu().numpy()
+
+    voice_name = name # whatever you want the name of the voice to be
+    output_path = voice_name + '.npz'
+    np.savez(output_path, fine_prompt=codes, coarse_prompt=codes[:2, :], semantic_prompt=semantic_tokens)
+
+    return voice_name + '.npz'
+
+css="""
+#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+"""
+
+title="""
+<div style="text-align: center;">
+    <h1>Voice Cloning for Bark Text-to-Audio</h1>
+    <p>This demo is an adaptation of the Serp-AI attempts to enable voice cloning using Bark</p>
+</div>
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(title)
+        audio_in = gr.Audio(label="Voice in to clone", source="upload", type="filepath")
+        transcript = gr.Textbox(label="Manual transcription of your audio")
+        name = gr.Textbox(label="Name your voice")
+
+        generate_btn = gr.Button("Clone voice !")
+
+        npz_file = gr.File()
+
+    generate_btn.click(clone_voice, inputs=[audio_in, transcript, name], outputs=[npe_file])
+
+demo.launch()