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"""Benchmark suite for voice model evaluation."""
import torch
import json
from pathlib import Path
from datetime import datetime
from typing import Dict, Any, List, Optional, Callable
import logging
from .metrics import MetricCalculator
logger = logging.getLogger(__name__)
class BenchmarkSuite:
"""
Comprehensive benchmark suite for voice models.
Evaluates models on multiple metrics and persists results.
"""
def __init__(self, output_dir: str = "results"):
"""
Initialize benchmark suite.
Args:
output_dir: Directory to save benchmark results
"""
self.output_dir = Path(output_dir)
self.output_dir.mkdir(parents=True, exist_ok=True)
self.metric_calculator = MetricCalculator()
self.results_history = []
logger.info(f"Initialized BenchmarkSuite with output_dir={output_dir}")
def run_benchmark(
self,
model_fn: Callable,
test_data: List[Dict[str, Any]],
model_name: str = "model",
checkpoint_path: Optional[str] = None
) -> Dict[str, Any]:
"""
Run complete benchmark on a model.
Args:
model_fn: Model inference function
test_data: List of test samples with audio and transcriptions
model_name: Name identifier for the model
checkpoint_path: Path to model checkpoint
Returns:
Dictionary containing all benchmark results
"""
logger.info(f"Running benchmark for {model_name} on {len(test_data)} samples")
start_time = datetime.now()
# Collect predictions and references
predictions = []
references = []
audio_pairs = []
latencies = []
for sample in test_data:
input_audio = sample['audio']
reference_text = sample.get('transcription', '')
reference_audio = sample.get('reference_audio', input_audio)
# Measure inference latency
import time
start = time.perf_counter()
output = model_fn(input_audio)
end = time.perf_counter()
latencies.append((end - start) * 1000)
# Extract prediction
if isinstance(output, dict):
pred_text = output.get('transcription', '')
pred_audio = output.get('audio', input_audio)
else:
pred_text = ''
pred_audio = output if isinstance(output, torch.Tensor) else input_audio
predictions.append(pred_text)
references.append(reference_text)
audio_pairs.append((pred_audio, reference_audio))
# Compute metrics
results = self.compute_metrics(
predictions=predictions,
references=references,
audio_pairs=audio_pairs
)
# Add latency metrics
results['inference_time_ms'] = sum(latencies) / len(latencies) if latencies else 0.0
results['samples_per_second'] = len(test_data) / (sum(latencies) / 1000) if latencies else 0.0
# Add metadata
results['timestamp'] = start_time.isoformat()
results['model_name'] = model_name
results['model_checkpoint'] = checkpoint_path
results['num_samples'] = len(test_data)
# Save results
self._save_results(results, model_name)
self.results_history.append(results)
logger.info(f"Benchmark complete. WER: {results.get('word_error_rate', 'N/A'):.4f}")
return results
def compute_metrics(
self,
predictions: List[str],
references: List[str],
audio_pairs: Optional[List[tuple]] = None
) -> Dict[str, float]:
"""
Compute all metrics for predictions.
Args:
predictions: List of predicted transcriptions
references: List of reference transcriptions
audio_pairs: Optional list of (generated, reference) audio pairs
Returns:
Dictionary of metric names and values
"""
metrics = {}
# Text-based metrics
if predictions and references:
try:
metrics['word_error_rate'] = self.metric_calculator.compute_word_error_rate(
predictions, references
)
except Exception as e:
logger.warning(f"Failed to compute WER: {e}")
metrics['word_error_rate'] = float('nan')
try:
metrics['character_error_rate'] = self.metric_calculator.compute_character_error_rate(
predictions, references
)
except Exception as e:
logger.warning(f"Failed to compute CER: {e}")
metrics['character_error_rate'] = float('nan')
# Audio-based metrics
if audio_pairs:
mcd_scores = []
pesq_scores = []
for gen_audio, ref_audio in audio_pairs:
if isinstance(gen_audio, torch.Tensor) and isinstance(ref_audio, torch.Tensor):
try:
mcd = self.metric_calculator.compute_mel_cepstral_distortion(
gen_audio, ref_audio
)
mcd_scores.append(mcd)
except Exception as e:
logger.warning(f"Failed to compute MCD: {e}")
try:
pesq = self.metric_calculator.compute_perceptual_quality(
gen_audio, ref_audio
)
pesq_scores.append(pesq)
except Exception as e:
logger.warning(f"Failed to compute PESQ: {e}")
if mcd_scores:
metrics['mel_cepstral_distortion'] = sum(mcd_scores) / len(mcd_scores)
if pesq_scores:
metrics['perceptual_evaluation_speech_quality'] = sum(pesq_scores) / len(pesq_scores)
return metrics
def _save_results(self, results: Dict[str, Any], model_name: str) -> None:
"""
Save benchmark results to file.
Args:
results: Results dictionary
model_name: Model identifier
"""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filename = f"benchmark_{model_name}_{timestamp}.json"
filepath = self.output_dir / filename
# Convert any non-serializable values
serializable_results = {}
for key, value in results.items():
if isinstance(value, (int, float, str, bool, type(None))):
serializable_results[key] = value
elif isinstance(value, datetime):
serializable_results[key] = value.isoformat()
else:
serializable_results[key] = str(value)
with open(filepath, 'w') as f:
json.dump(serializable_results, f, indent=2)
logger.info(f"Results saved to {filepath}")
def load_results(self, filepath: str) -> Dict[str, Any]:
"""
Load benchmark results from file.
Args:
filepath: Path to results file
Returns:
Results dictionary
"""
with open(filepath, 'r') as f:
results = json.load(f)
return results
def get_latest_results(self, model_name: Optional[str] = None) -> Optional[Dict[str, Any]]:
"""
Get the most recent benchmark results.
Args:
model_name: Optional model name filter
Returns:
Latest results dictionary or None
"""
if not self.results_history:
return None
if model_name:
filtered = [r for r in self.results_history if r.get('model_name') == model_name]
return filtered[-1] if filtered else None
return self.results_history[-1]
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