DiffRhythm2 / app.py
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Deploy HARP wrapper via model agent
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from __future__ import annotations
import os
import time
import urllib.request
import gradio as gr
from pyharp import *
from gradio_client import Client, handle_file
_BACKEND_SPACE = "ASLP-lab/DiffRhythm2"
_BACKEND_API_NAME = "/infer_music"
_BACKEND_TOKEN_ENV = "HF_TOKEN"
_ACCEPT_USER_TOKEN = False
# How many times to wake+retry a sleeping backend, and how long to wait for
# it to boot (a free Space cold start can take a few minutes).
_CALL_RETRIES = int(os.environ.get("BACKEND_CALL_RETRIES", "4"))
_WAKE_TIMEOUT = float(os.environ.get("BACKEND_WAKE_TIMEOUT", "420"))
_client = None
def _backend_client():
# Lazily create and cache one warm connection using this Space's own
# token (from the HF_TOKEN secret) or anonymous if none is set. User
# tokens are NOT cached here -- they get a fresh per-call connection.
global _client
if _client is None:
_token = os.environ.get(_BACKEND_TOKEN_ENV) or None
_client = Client(_BACKEND_SPACE, hf_token=_token)
return _client
def _reset_client():
# Drop the cached connection so the next attempt reconnects to a Space
# that has since finished waking.
global _client
_client = None
def _make_conn(tok):
tok = (tok or '').strip()
if tok:
return Client(_BACKEND_SPACE, hf_token=tok)
return _backend_client()
def _space_url(space):
slug = space.strip().lower().replace('/', '-').replace('_', '-')
return f'https://{slug}.hf.space/'
def _is_cold_start(message):
# Errors that mean 'the backend was asleep/booting', worth waking+retrying
# (vs. a real application error, which we surface immediately).
_low = (message or '').lower()
return any(s in _low for s in (
'read operation timed out', 'timed out', 'timeout', 'starting',
'building', 'not ready', 'no application', 'connection', '503', '502',
))
def _wake_backend():
# A sleeping Space boots when its URL is hit; poll until it answers (or
# the budget expires) so the retried call lands on a running backend.
_url = _space_url(_BACKEND_SPACE)
_deadline = time.time() + _WAKE_TIMEOUT
_delay = 5.0
while time.time() < _deadline:
try:
_req = urllib.request.Request(_url, headers={'User-Agent': 'harp-frontend'})
with urllib.request.urlopen(_req, timeout=30) as _resp:
if getattr(_resp, 'status', 200) < 500:
return True
except Exception:
pass
time.sleep(_delay)
_delay = min(_delay * 1.5, 30.0)
return False
def _quota_hint(message):
# Turn a backend error into an actionable message.
# NOTE: 'message' is the backend's error text; it never contains our token.
_low = (message or "").lower()
if "quota" in _low or "zerogpu" in _low:
if _ACCEPT_USER_TOKEN:
return (
"The backend's ZeroGPU quota is exhausted for the identity making "
"this call. Paste your own Hugging Face token in the token field "
"(read scope) so usage is attributed to your account."
)
return (
"The backend's ZeroGPU quota is exhausted. This Space's calls are "
"anonymous unless an HF_TOKEN secret is set (Settings -> Variables "
"and secrets); use a token from a PRO account or a ZeroGPU-enabled org."
)
# Opaque backend failure: the Space raised an exception it refuses to
# expose (it runs with show_error=False), so all we get is a generic
# 'Internal Gradio error'. The frontend can't fix a server-side crash --
# point the user at where the real cause lives.
if (
"internal gradio error" in _low
or "internal server error" in _low
or "apperror" in _low
or _low.strip() in ("", "none")
):
_hint = (
"The backend Space raised an error it did not expose (it runs with "
"show_error disabled), so the real cause is only in the backend "
"Space's Logs tab (" + _BACKEND_SPACE + ")."
)
if _ACCEPT_USER_TOKEN:
_hint += (
" If it is a ZeroGPU Space, an anonymous call can fail this way -- "
"paste a Hugging Face token in the token field and retry."
)
return _hint
return message or "Backend call failed."
model_card = ModelCard(
name="Diffrhythm2",
description="TODO: describe this model.",
author="ASLP-lab",
tags=[],
)
def process_fn(lrc, audio_prompt, text_prompt, seed, randomize_seed, steps, cfg_strength, file_type):
_tok = ''
# Call the backend, waking it and retrying if it was asleep (a cold
# start otherwise fails the first hit with 'read operation timed out').
_raw = None
for _attempt in range(_CALL_RETRIES + 1):
try:
_conn = _make_conn(_tok)
_raw = _conn.predict(
lrc,
(handle_file(audio_prompt) if audio_prompt else None),
text_prompt,
seed,
randomize_seed,
steps,
cfg_strength,
file_type,
'euler',
api_name="/infer_music",
)
break
except Exception as _exc: # never surfaces the token
if _attempt < _CALL_RETRIES and _is_cold_start(str(_exc)):
_reset_client()
_wake_backend()
continue
raise gr.Error(_quota_hint(str(_exc)))
_values = list(_raw) if isinstance(_raw, (list, tuple)) else [_raw]
_detail = " | ".join(str(_v) for _v in _values if isinstance(_v, str) and _v.strip())
_out_audio_result = _values[0] if len(_values) > 0 else None
if not _out_audio_result:
raise gr.Error(_detail or "The backend Space returned no 'audio_result' output. Check the backend Space's logs; if it uses ZeroGPU it may need a moment to warm up.")
return _out_audio_result
with gr.Blocks() as demo:
input_components = [
gr.Textbox(label="Lyrics", value="[start]\n[intro]\n[verse]\nThought I heard your voice yesterday\nWhen I turned around to say\nThat I loved you baby\nI realize it was juss my mind\nPlayed tricks on me\nAnd it seems colder lately at night\nAnd I try to sleep with the lights on\nEvery time the phone rings\nI pray to God it's you\nAnd I just can't believe\nThat we're through\n[chorus]\nI miss you\nThere's no other way to say it\nAnd I can't deny it\nI miss you\nIt's so easy to see\nI miss you and me\n[verse]\nIs it turning over this time\nHave we really changed our minds about each other's love\nAll the feelings that we used to share\nI refuse to believe\nThat you don't care\n[chorus]\nI miss you\nThere's no other way to say it\nAnd I and I can't deny it\nI miss you\n[verse]\nIt's so easy to see\nI've got to gather myself as together\nI've been through worst kinds of weather\nIf it's over now\n[outro]"),
gr.Audio(type="filepath", label="Audio Prompt"),
gr.Textbox(label="Text Prompt", value="Pop, Piano, Bass, Drums, Happy"),
gr.Slider(minimum=0.0, maximum=10.0, step=1.0, value=0, label="Seed"),
gr.Checkbox(value=True, label="Randomize seed"),
gr.Slider(minimum=0.0, maximum=32.0, step=1.0, value=16, label="Diffusion Steps"),
gr.Slider(minimum=1.3, maximum=100, step=1, value=1.3, label="CFG Strength"),
gr.Dropdown(choices=["mp3"], value="mp3", label="Output Format"),
]
output_components = [
gr.Audio(type="filepath", label="Audio Result"),
]
build_endpoint(
model_card=model_card,
input_components=input_components,
output_components=output_components,
process_fn=process_fn,
)
demo.queue().launch(share=True, show_error=False, pwa=True)