Spaces:
Running on Zero
Running on Zero
File size: 4,316 Bytes
ae0617f 3cd0dfd 24ebea4 3cd0dfd ae0617f 0ca1335 ae0617f ac877ac ae0617f bef4266 99febd9 bef4266 861914a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | from __future__ import annotations
import gradio as gr
try:
import spaces
except ImportError: # 'spaces' is only provided by Hugging Face Spaces
import types as _types
def _gpu(*args, **kwargs):
if len(args) == 1 and callable(args[0]) and not kwargs:
return args[0]
def _decorator(func):
return func
return _decorator
spaces = _types.SimpleNamespace(GPU=_gpu)
from pyharp import *
try: # torch>=2.6 flipped torch.load(weights_only) to True; legacy ckpts need False
import torch as _torch
if getattr(_torch.load, "__harp_compat__", False) is False:
_torch_load_orig = _torch.load
def _torch_load_compat(*args, **kwargs):
kwargs.setdefault("weights_only", False)
return _torch_load_orig(*args, **kwargs)
_torch_load_compat.__harp_compat__ = True
_torch.load = _torch_load_compat
except Exception: # torch not installed / unexpected API -- nothing to patch
pass
from bark import generate_audio, preload_models, SAMPLE_RATE
from scipy.io.wavfile import write as write_wav
import tempfile
import os
# Download and load all models once
preload_models()
model_card = ModelCard(
name="Bark",
description=(
"Bark is a transformer-based text-to-audio model created by Suno. Bark "
"can generate highly realistic, multilingual speech as well as other "
"audio - including music, background noise and simple sound effects. "
"The model can also produce nonverbal communications like laughing, "
"sighing and crying. Repository: https://github.com/suno-ai/bark"
),
author="suno-ai",
tags=[Subcategory.TEXT_TO_SPEECH, 'multilingual'],
)
def process_fn(text_prompt, history_prompt, text_temp, waveform_temp):
# Checked before the GPU is reserved, so a request the model cannot take spends none of the
# caller's ZeroGPU allowance
if not (text_prompt or "").strip():
raise gr.Error("Enter a text prompt.")
return generate(text_prompt, history_prompt, text_temp, waveform_temp)
@spaces.GPU
def generate(text_prompt, history_prompt, text_temp, waveform_temp):
history_prompt_val = history_prompt if history_prompt != "None" else None
# Generate audio from text
audio_array = generate_audio(
text_prompt,
history_prompt=history_prompt_val,
text_temp=text_temp,
waveform_temp=waveform_temp,
silent=True, # Disable progress bar for Gradio app
output_full=False # Only return the audio array
)
# Save audio to a temporary WAV file
output_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
output_file.close()
write_wav(output_file.name, SAMPLE_RATE, audio_array)
return output_file.name
if __name__ == "__main__":
with gr.Blocks() as demo:
input_components = [
gr.Textbox(label="Text Prompt", info="The text to be converted into audio. Bark supports multilingual speech, music, and nonverbal communications.").harp_required(True),
gr.Dropdown(choices=["None", "announcer", "en_speaker_0", "en_speaker_1", "en_speaker_2", "en_speaker_3", "en_speaker_4", "en_speaker_5", "en_speaker_6", "en_speaker_7", "en_speaker_8", "en_speaker_9"], value="en_speaker_0", label="Voice Prompt (Speaker)", info="Choose a speaker to use as a voice prompt for audio cloning. 'None' will use a default voice."),
gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.7, label="Text Generation Temperature", info="Controls the diversity of text token generation (1.0 more diverse, 0.0 more conservative)."),
gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.7, label="Waveform Generation Temperature", info="Controls the diversity of waveform token generation (1.0 more diverse, 0.0 more conservative)."),
]
output_components = [
gr.Audio(type="filepath", label="Generated Audio").set_info((
"The spoken or sung result, at 24 kHz."
)),
]
build_endpoint(
model_card=model_card,
input_components=input_components,
output_components=output_components,
process_fn=process_fn,
)
demo.queue().launch(show_error=True, pwa=True)
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