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import os
import subprocess
import tempfile
import time
from functools import lru_cache
from pathlib import Path
import gradio as gr
try:
import spaces # type: ignore[import-not-found]
except ImportError:
class _SpacesFallback:
@staticmethod
def GPU(func):
return func
spaces = _SpacesFallback()
from src.hf_inference import MossAudioHFInference, read_env_model_id, resolve_device
TITLE = "MOSS-Audio-8B-Thinking Demo"
DEFAULT_QUESTION = "Describe this audio."
DEFAULT_MAX_NEW_TOKENS = 1024
DEFAULT_TEMPERATURE = 1.0
DEFAULT_TOP_P = 1.0
DEFAULT_TOP_K = 50
VIDEO_EXTENSIONS = {".mp4"}
@lru_cache(maxsize=2)
def get_inference(model_name_or_path: str, device: str) -> MossAudioHFInference:
return MossAudioHFInference(
model_name_or_path=model_name_or_path,
device=device,
torch_dtype="auto",
enable_time_marker=True,
)
def format_status(model_name_or_path: str, device: str, elapsed_seconds: float) -> str:
return (
f"Model: `{model_name_or_path}` \n"
f"Device: `{device}` \n"
f"Elapsed: `{elapsed_seconds:.2f}s`"
)
def convert_media_to_mp3(media_path: str, output_path: str) -> None:
command = [
"ffmpeg",
"-y",
"-i",
media_path,
"-vn",
"-acodec",
"libmp3lame",
output_path,
]
try:
subprocess.run(
command,
check=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.PIPE,
text=True,
)
except subprocess.CalledProcessError as exc:
raise gr.Error(
f"Failed to extract audio from the uploaded media. Please make sure the mp4 file is valid and decodable.\n{exc.stderr}"
) from exc
def resolve_media_path(audio_path: str | None, video_path: str | None) -> str | None:
if video_path:
return video_path
return audio_path
@spaces.GPU
def run_inference(
audio_path: str | None,
video_path: str | None,
question: str,
max_new_tokens: int,
temperature: float,
top_p: float,
top_k: int,
):
prompt = (question or "").strip() or DEFAULT_QUESTION
model_name_or_path = read_env_model_id()
device = resolve_device()
try:
inference = get_inference(model_name_or_path, device)
except Exception as exc: # pragma: no cover - runtime environment dependent
raise gr.Error(
f"Failed to load the model. Please check the weights path or Hugging Face download status.\n{exc}"
) from exc
media_path = resolve_media_path(audio_path, video_path)
try:
started_at = time.perf_counter()
with tempfile.TemporaryDirectory(prefix="moss-audio-") as temp_dir:
prepared_audio_path = media_path
if media_path and Path(media_path).suffix.lower() in VIDEO_EXTENSIONS:
prepared_audio_path = os.path.join(temp_dir, "input.mp3")
convert_media_to_mp3(media_path, prepared_audio_path)
answer = inference.generate(
question=prompt,
audio_path=prepared_audio_path,
max_new_tokens=max_new_tokens,
do_sample=temperature > 0,
temperature=temperature,
top_p=top_p,
top_k=top_k,
)
elapsed_seconds = time.perf_counter() - started_at
except Exception as exc: # pragma: no cover - runtime environment dependent
raise gr.Error(
f"Inference failed. Please make sure the uploaded file is readable and the format is supported.\n{exc}"
) from exc
return answer, format_status(model_name_or_path, device, elapsed_seconds)
with gr.Blocks(title=TITLE) as demo:
gr.Markdown(f"# {TITLE}")
with gr.Row():
with gr.Column(scale=5):
audio_input = gr.Audio(
label="Audio",
sources=["upload", "microphone"],
type="filepath",
)
with gr.Accordion("Optional Video Input (.mp4)", open=False):
gr.Markdown(
"Upload an mp4 only when needed. If a video is provided, its audio track will be extracted and used for inference."
)
video_input = gr.File(
label="Video File",
file_types=[".mp4"],
type="filepath",
)
question_input = gr.Textbox(
label="Prompt",
lines=4,
value=DEFAULT_QUESTION,
placeholder="For example: Please transcribe this audio. Describe the sounds in this clip. What emotion does the speaker convey?",
)
with gr.Accordion("Advanced Settings", open=False):
max_new_tokens_input = gr.Slider(
minimum=64,
maximum=2048,
value=DEFAULT_MAX_NEW_TOKENS,
step=32,
label="Max New Tokens",
)
temperature_input = gr.Slider(
minimum=0.0,
maximum=1.5,
value=DEFAULT_TEMPERATURE,
step=0.1,
label="Temperature",
)
top_p_input = gr.Slider(
minimum=0.1,
maximum=1.0,
value=DEFAULT_TOP_P,
step=0.05,
label="Top-p",
)
top_k_input = gr.Slider(
minimum=1,
maximum=100,
value=DEFAULT_TOP_K,
step=1,
label="Top-k",
)
with gr.Row():
submit_btn = gr.Button("Generate", variant="primary")
gr.ClearButton(
[
audio_input,
video_input,
question_input,
max_new_tokens_input,
temperature_input,
top_p_input,
top_k_input,
],
value="Clear",
)
with gr.Column(scale=5):
output_text = gr.Textbox(label="Output", lines=16)
status_text = gr.Markdown("Waiting for input.")
gr.Examples(
examples=[
["Describe this audio."],
["Please transcribe this audio."],
["What is happening in this audio clip?"],
["Describe the speaker's voice characteristics in detail."],
["What emotion does the speaker convey?"],
],
inputs=[question_input],
label="Prompt Examples",
)
submit_btn.click(
fn=run_inference,
inputs=[
audio_input,
video_input,
question_input,
max_new_tokens_input,
temperature_input,
top_p_input,
top_k_input,
],
outputs=[output_text, status_text],
)
if __name__ == "__main__":
server_name = os.environ.get("MOSS_AUDIO_SERVER_NAME", "0.0.0.0")
server_port = int(os.environ.get("MOSS_AUDIO_SERVER_PORT", "7860"))
demo.queue(max_size=8).launch(
server_name=server_name,
server_port=server_port,
ssr_mode=False,
)
|