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import time
from concurrent.futures import TimeoutError as FutureTimeoutError
from datetime import datetime
from pathlib import Path
from typing import Any
from gradio_client import Client, handle_file
from src.constants import (
PARAKEET_V3,
PYANNOTE_COMMUNITY_1,
WHISPER_LARGE_V3_TURBO,
)
MODEL_API_BY_LABEL = {
WHISPER_LARGE_V3_TURBO: "/transcribe_whisper_large_v3_turbo",
PARAKEET_V3: "/transcribe_parakeet_v3",
}
PYANNOTE_API_NAME = "/diarize_pyannote_community_1"
PARAKEET_API_NAME = "/transcribe_parakeet_v3"
def _safe_name(value: str) -> str:
return value.replace("/", "_").replace(" ", "_").replace("(", "").replace(")", "").lower()
def _to_model_options_json(model_options: str | dict[str, Any] | None) -> str | None:
if model_options is None:
return None
if isinstance(model_options, str):
return model_options
return json.dumps(model_options)
def _normalize_model_options_for_model(model_label: str, model_options: str | dict[str, Any] | None) -> str | dict[str, Any] | None:
if not isinstance(model_options, dict):
return model_options
normalized = dict(model_options)
if model_label == PARAKEET_V3:
normalized.pop("batch_size", None)
return normalized
def _leaderboard_rows(results: list[dict[str, Any]]) -> list[dict[str, Any]]:
ok_items = [r for r in results if r.get("status") == "ok"]
def key_fn(item: dict[str, Any]) -> float:
payload = item.get("result") or {}
timing = payload.get("zerogpu_timing") or {}
value = timing.get("gpu_window_seconds")
return float("inf") if value is None else float(value)
ranked = sorted(ok_items, key=key_fn)
return [
{
"model": item["model"],
"api_name": item["api_name"],
"gpu_window_seconds": ((item.get("result") or {}).get("zerogpu_timing") or {}).get("gpu_window_seconds"),
"inference_seconds": ((item.get("result") or {}).get("zerogpu_timing") or {}).get("inference_seconds"),
"client_wall_clock_seconds": item.get("client_wall_clock_seconds"),
}
for item in ranked
]
def _save_benchmark_outputs(result_obj: dict[str, Any], output_dir: str | Path | None) -> dict[str, Any]:
root = Path(output_dir) if output_dir else Path("benchmark_outputs")
run_dir = root / datetime.now().strftime("%Y%m%d_%H%M%S")
run_dir.mkdir(parents=True, exist_ok=True)
per_model_files: dict[str, str] = {}
for item in result_obj.get("results", []):
model = item.get("model", "unknown")
filename = f"{_safe_name(model)}.json"
file_path = run_dir / filename
payload = item.get("result") if item.get("status") == "ok" else item
file_path.write_text(json.dumps(payload, indent=2, ensure_ascii=False))
per_model_files[model] = str(file_path)
summary = {
"space": result_obj.get("space"),
"audio_file": result_obj.get("audio_file"),
"task": result_obj.get("task"),
"language": result_obj.get("language"),
"models": result_obj.get("models"),
"benchmark_timing": result_obj.get("benchmark_timing"),
"leaderboard_by_gpu_window_seconds": result_obj.get("leaderboard_by_gpu_window_seconds"),
"summary": [
{
"model": r.get("model"),
"status": r.get("status"),
"api_name": r.get("api_name"),
"client_wall_clock_seconds": r.get("client_wall_clock_seconds"),
"gpu_window_seconds": ((r.get("result") or {}).get("zerogpu_timing") or {}).get("gpu_window_seconds"),
"inference_seconds": ((r.get("result") or {}).get("zerogpu_timing") or {}).get("inference_seconds"),
"error": r.get("error"),
}
for r in result_obj.get("results", [])
],
}
stats_path = run_dir / "benchmark_stats.json"
stats_path.write_text(json.dumps(summary, indent=2, ensure_ascii=False))
return {
"run_dir": str(run_dir),
"per_model_files": per_model_files,
"benchmark_stats_file": str(stats_path),
}
def run_pyannote_api(
space: str,
audio_file: str,
model_options_by_model: dict[str, str | dict[str, Any]] | None = None,
model_options: str | dict[str, Any] | None = None,
hf_token: str | None = None,
request_timeout_s: float = 1800.0,
result_timeout_s: float | None = 7200.0,
save_outputs: bool = True,
output_dir: str | Path | None = None,
) -> dict[str, Any]:
"""Benchmark-style wrapper for a single pyannote diarization API call."""
client = Client(space, token=hf_token, httpx_kwargs={"timeout": request_timeout_s})
options_json = _to_model_options_json(model_options)
if model_options_by_model and PYANNOTE_COMMUNITY_1 in model_options_by_model:
options_json = _to_model_options_json(model_options_by_model[PYANNOTE_COMMUNITY_1])
started_at = time.perf_counter()
call_start = time.perf_counter()
try:
job = client.submit(
audio_file=handle_file(audio_file),
model_options_json=options_json,
api_name=PYANNOTE_API_NAME,
)
response = job.result(timeout=result_timeout_s)
call_end = time.perf_counter()
result_item: dict[str, Any] = {
"model": PYANNOTE_COMMUNITY_1,
"api_name": PYANNOTE_API_NAME,
"status": "ok",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"effective_model_options_json": options_json,
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"result": response,
}
except FutureTimeoutError:
call_end = time.perf_counter()
result_item = {
"model": PYANNOTE_COMMUNITY_1,
"api_name": PYANNOTE_API_NAME,
"status": "error",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"effective_model_options_json": options_json,
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"error": f"Pyannote call timed out after {result_timeout_s}s. Increase result_timeout_s for long audio.",
}
except Exception as exc:
call_end = time.perf_counter()
result_item = {
"model": PYANNOTE_COMMUNITY_1,
"api_name": PYANNOTE_API_NAME,
"status": "error",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"effective_model_options_json": options_json,
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"error": str(exc),
}
finished_at = time.perf_counter()
payload: dict[str, Any] = {
"space": space,
"audio_file": audio_file,
"task": "diarize",
"model_options_json": options_json,
"model_options_by_model": model_options_by_model,
"models": [PYANNOTE_COMMUNITY_1],
"benchmark_timing": {
"total_client_wall_clock_seconds": round(finished_at - started_at, 4),
},
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"results": [result_item],
"leaderboard_by_gpu_window_seconds": _leaderboard_rows([result_item]),
}
if save_outputs:
payload["saved_outputs"] = _save_benchmark_outputs(payload, output_dir=output_dir)
return payload
def run_parakeet_then_pyannote(
space: str,
audio_file: str,
parakeet_model_options: str | dict[str, Any] | None = None,
pyannote_model_options: str | dict[str, Any] | None = None,
pyannote_hf_token: str | None = None,
hf_token: str | None = None,
request_timeout_s: float = 1800.0,
result_timeout_s: float | None = 7200.0,
save_outputs: bool = True,
output_dir: str | Path | None = None,
) -> dict[str, Any]:
"""Run Parakeet transcription then Pyannote diarization and aggregate ZeroGPU timing."""
client = Client(space, token=hf_token, httpx_kwargs={"timeout": request_timeout_s})
parakeet_model_options = _normalize_model_options_for_model(PARAKEET_V3, parakeet_model_options)
pyannote_model_options = _normalize_model_options_for_model(PYANNOTE_COMMUNITY_1, pyannote_model_options)
if pyannote_hf_token:
if pyannote_model_options is None:
pyannote_model_options = {}
elif isinstance(pyannote_model_options, str):
parsed = json.loads(pyannote_model_options)
if not isinstance(parsed, dict):
raise ValueError("pyannote_model_options string must decode to a JSON object.")
pyannote_model_options = parsed
if isinstance(pyannote_model_options, dict):
pyannote_model_options = dict(pyannote_model_options)
pyannote_model_options.setdefault("hf_token", pyannote_hf_token)
parakeet_options_json = _to_model_options_json(parakeet_model_options)
pyannote_options_json = _to_model_options_json(pyannote_model_options)
def _call_api(api_name: str, call_kwargs: dict[str, Any]) -> dict[str, Any]:
call_start = time.perf_counter()
try:
job = client.submit(api_name=api_name, **call_kwargs)
response = job.result(timeout=result_timeout_s)
call_end = time.perf_counter()
return {
"status": "ok",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"result": response,
}
except FutureTimeoutError:
call_end = time.perf_counter()
return {
"status": "error",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"error": f"Call timed out after {result_timeout_s}s.",
}
except Exception as exc:
call_end = time.perf_counter()
return {
"status": "error",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"error": str(exc),
}
started_at = time.perf_counter()
parakeet_call = _call_api(
api_name=PARAKEET_API_NAME,
call_kwargs={
"audio_file": handle_file(audio_file),
"task": "transcribe",
"language": None,
"initial_prompt": None,
"postprocess_prompt": None,
"model_options_json": parakeet_options_json,
},
)
parakeet_call.update(
{
"model": PARAKEET_V3,
"api_name": PARAKEET_API_NAME,
"effective_model_options_json": parakeet_options_json,
}
)
pyannote_call = _call_api(
api_name=PYANNOTE_API_NAME,
call_kwargs={
"audio_file": handle_file(audio_file),
"model_options_json": pyannote_options_json,
},
)
pyannote_call.update(
{
"model": PYANNOTE_COMMUNITY_1,
"api_name": PYANNOTE_API_NAME,
"effective_model_options_json": pyannote_options_json,
}
)
results: list[dict[str, Any]] = [parakeet_call, pyannote_call]
finished_at = time.perf_counter()
total_zerogpu_gpu_window_seconds = 0.0
total_zerogpu_inference_seconds = 0.0
for item in results:
if item.get("status") != "ok":
continue
timing = ((item.get("result") or {}).get("zerogpu_timing") or {})
total_zerogpu_gpu_window_seconds += float(timing.get("gpu_window_seconds", 0.0))
total_zerogpu_inference_seconds += float(timing.get("inference_seconds", 0.0))
payload: dict[str, Any] = {
"space": space,
"audio_file": audio_file,
"task": "transcribe+diarize",
"models": [PARAKEET_V3, PYANNOTE_COMMUNITY_1],
"model_options_by_model": {
PARAKEET_V3: parakeet_options_json,
PYANNOTE_COMMUNITY_1: pyannote_options_json,
},
"pyannote_hf_token_provided": bool(pyannote_hf_token),
"timeouts": {
"request_timeout_s": request_timeout_s,
"result_timeout_s": result_timeout_s,
},
"benchmark_timing": {
"total_client_wall_clock_seconds": round(finished_at - started_at, 4),
},
"total_zerogpu_timing": {
"gpu_window_seconds": round(total_zerogpu_gpu_window_seconds, 4),
"inference_seconds": round(total_zerogpu_inference_seconds, 4),
},
"results": results,
"leaderboard_by_gpu_window_seconds": _leaderboard_rows(results),
}
if save_outputs:
payload["saved_outputs"] = _save_benchmark_outputs(payload, output_dir=output_dir)
return payload
def run_all_model_apis(
space: str,
audio_file: str,
task: str = "transcribe",
language: str | None = None,
initial_prompt: str | None = None,
postprocess_prompt: str | None = None,
model_options: str | dict[str, Any] | None = None,
model_options_by_model: dict[str, str | dict[str, Any]] | None = None,
models: list[str] | None = None,
hf_token: str | None = None,
save_outputs: bool = True,
output_dir: str | Path | None = None,
) -> dict[str, Any]:
"""Run each model-specific API endpoint one by one and collect full outputs.
Designed for use from IPython notebooks/scripts.
Use model_options_by_model for per-model tuning in a single benchmark run.
"""
if models is None:
model_sequence = list(MODEL_API_BY_LABEL.keys())
else:
invalid = [m for m in models if m not in MODEL_API_BY_LABEL]
if invalid:
raise ValueError(f"Unsupported models requested: {invalid}")
model_sequence = models
client = Client(space, token=hf_token)
options_json = _to_model_options_json(model_options)
started_at = time.perf_counter()
results: list[dict[str, Any]] = []
for model in model_sequence:
api_name = MODEL_API_BY_LABEL[model]
effective_options_source: str | dict[str, Any] | None = model_options
if model_options_by_model and model in model_options_by_model:
effective_options_source = model_options_by_model[model]
normalized_options = _normalize_model_options_for_model(model, effective_options_source)
effective_options_json = _to_model_options_json(normalized_options)
call_start = time.perf_counter()
try:
response = client.predict(
audio_file=handle_file(audio_file),
task=task,
language=language,
initial_prompt=initial_prompt,
postprocess_prompt=postprocess_prompt,
model_options_json=effective_options_json,
api_name=api_name,
)
call_end = time.perf_counter()
results.append(
{
"model": model,
"api_name": api_name,
"status": "ok",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"effective_model_options_json": effective_options_json,
"result": response,
}
)
except Exception as exc:
call_end = time.perf_counter()
results.append(
{
"model": model,
"api_name": api_name,
"status": "error",
"client_wall_clock_seconds": round(call_end - call_start, 4),
"effective_model_options_json": effective_options_json,
"error": str(exc),
}
)
finished_at = time.perf_counter()
payload = {
"space": space,
"audio_file": audio_file,
"task": task,
"language": language,
"initial_prompt": initial_prompt,
"postprocess_prompt": postprocess_prompt,
"model_options_json": options_json,
"model_options_by_model": model_options_by_model,
"models": model_sequence,
"benchmark_timing": {
"total_client_wall_clock_seconds": round(finished_at - started_at, 4),
},
"results": results,
"leaderboard_by_gpu_window_seconds": _leaderboard_rows(results),
}
if save_outputs:
payload["saved_outputs"] = _save_benchmark_outputs(payload, output_dir=output_dir)
return payload
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