perf: GPU pipeline optimization — 1.28x CUDA, 1.17x MPS, memory-safe batching

[davidamacey]

Summary

This PR optimizes the GPU pipeline for speaker diarization embedding extraction, achieving:

• CUDA: 1.28x overall speedup and 1.44x embedding stage speedup with constant, predictable VRAM regardless of input length
• MPS (Apple Silicon): 1.17x overall speedup and 1.21x embedding stage speedup with native MPS FFT
• All devices: 115x reduction in peak CPU RAM for long files (58.8GB to 39MB for 4.7h/21-speaker)

These changes target the embedding extraction stage in get_embeddings(), which accounts for 60-85% of total diarization time. Segmentation, clustering, and reconstruction are unchanged.

Motivation: We discovered these bottlenecks while building OpenTranscribe, an open-source transcription application that processes thousands of audio files. Diarization was consistently the slowest stage, and GPU utilization during embedding extraction was well below capacity. Community issues #1614, #1403, #1753, #1652, and #1566 report similar observations — high CPU utilization, low GPU utilization, and embedding extraction dominating runtime.

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Benchmark Results (CUDA)

Hardware: NVIDIA RTX A6000 (48GB VRAM)
Base commit: 78c0d16a (v4.0.4)
Test suite: 5 audio files ranging from 0.5h to 4.7h (12+ hours total, 3-21 speakers)
Methodology: 5 runs per file, results show mean (std dev 0.3s on 0.5h file)

Note on VRAM measurements: Our benchmark GPU had other models resident in memory (~5.6GB baseline). The absolute VRAM numbers reflect this shared environment. The key finding is the delta: stock PyAnnote adds 2-12GB on top of baseline (unpredictably), while optimized adds near-zero additional VRAM.

Overall Pipeline Performance

Audio Length	Speakers	Stock	Optimized	Speedup
0.5h (1,899s)	4	29.4s	21.4s	1.37x
1.0h (3,758s)	5	56.5s	42.3s	1.34x
2.2h (7,998s)	3	124.0s	95.0s	1.31x
3.2h (11,495s)	3	184.6s	142.6s	1.29x
4.7h (17,044s)	21	323.7s	261.9s	1.24x
Total (12.2h)		718.2s	563.2s	1.28x

Embedding Stage Performance (where optimizations apply)

Audio Length	Stock	Optimized	Speedup
0.5h	25.2s	16.9s	1.49x
1.0h	48.6s	33.7s	1.44x
2.2h	102.6s	71.3s	1.44x
3.2h	147.6s	103.1s	1.43x
4.7h	218.8s	155.5s	1.41x

The overall speedup (1.28x) is less than the embedding speedup (1.44x) because clustering and segmentation are unchanged and account for ~15-40% of total time depending on speaker count.

CUDA Statistical Validation (0.5h file, 5 runs)

Metric	Stock	Optimized
Mean	29.4s	21.2s
Std dev	—	0.3s
Min	—	21.0s
Max	—	21.8s
Speakers	4	4 (consistent all runs)
Segments	597	383 (consistent all runs)
Peak GPU	~7-17GB	3,883 MB
Steady GPU	—	39 MB
Process RSS	—	3,279 MB

VRAM Behavior

Audio Length	Stock VRAM Delta	Optimized VRAM Delta
0.5h	+1,976 MB	+0 MB
1.0h	+11,072 MB	+0 MB
2.2h	+11,718 MB	+0 MB
3.2h	+1,978 MB	+0 MB
4.7h	+11,136 MB	+0 MB

Stock VRAM spikes unpredictably (2-12GB above baseline depending on file). Optimized maintains a constant footprint with near-zero additional allocation. This is critical for multi-worker deployments where VRAM budgets must be predictable.

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Benchmark Results (MPS — Apple Silicon)

Hardware: Mac Studio M2 Max (38 GPU cores, Metal 3, 32GB unified memory)
PyTorch: 2.10.0
Base commit: 78c0d16a (v4.0.4)
Test suite: Same 5 audio files as CUDA benchmarks
Methodology: 5 runs for statistical validation on 0.5h file; single run for full suite

Overall Pipeline Performance

Audio Length	Speakers	Stock	Optimized	Speedup
0.5h (1,899s)	4-5	48.1s	41.3s	1.17x
1.0h (3,758s)	5	95.3s	81.8s	1.17x
2.2h (7,998s)	3	205.7s	176.8s	1.16x
3.2h (11,495s)	3	298.2s	256.5s	1.16x
4.7h (17,044s)	8	472.7s	454.9s	1.04x
Total (12.2h)		1,120.0s	1,011.3s	1.11x

Embedding Stage Performance

Audio Length	Stock	Optimized	Speedup
0.5h	42.4s	35.1s	1.21x
1.0h	84.4s	69.8s	1.21x
2.2h	180.7s	149.9s	1.21x
3.2h	260.1s	215.8s	1.21x
4.7h	385.8s	355.1s	1.09x

MPS Statistical Validation (0.5h file, 5 runs each)

Metric	Stock	Optimized
Mean	47.7s	40.8s
Std dev	0.1s	0.1s
Min	47.6s	40.7s
Max	47.7s	41.1s
Speedup	—	1.17x
Speakers	4	4 (consistent all runs)
Segments	388	390 (consistent all runs)
Peak RSS	1,544 MB	2,465 MB

MPS Performance Notes

• MPS speedup (1.17x) is lower than CUDA (1.28x) because CUDA-specific optimizations (TF32, double-buffered stream prefetch, pinned memory) are not applicable to unified memory architecture
• The 4.7h file shows lower speedup (1.04x) because clustering time dominates with 8 speakers
• MPS run-to-run variance is very low (std 0.1s) — results are highly reproducible
• First-run JIT warmup adds ~50s on MPS (Metal shader compilation); subsequent runs are consistent

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CPU RAM Safety Fix (Critical for Low-Memory Machines)

The stock code uses repeat_interleave() to pre-materialize all chunk x speaker waveform combinations before the embedding loop. This creates a massive CPU tensor that scales with both file length and speaker count:

File	Speakers	Stock repeat_interleave RAM	Optimized per-batch RAM
0.5h	4	4.5 GB	39 MB
1.0h	5	11.2 GB	39 MB
2.2h	3	14.3 GB	39 MB
4.7h	21	58.8 GB	39 MB

A 4.7h file with 21 speakers would crash any machine with less than 64GB RAM. This PR replaces repeat_interleave() with on-the-fly batch indexing that uses constant 39MB regardless of file length or speaker count:

# Before: materializes ALL chunk x speaker combinations (58.8GB for 4.7h/21spk)
flat_waveforms = all_chunks.repeat_interleave(num_speakers, dim=0).unsqueeze(1)

# After: indexes into all_chunks per batch (39MB constant)
def _get_batch_waveforms(start, end):
    chunk_indices = torch.arange(start, end) // num_speakers
    return all_chunks[chunk_indices].unsqueeze(1)

The all_chunks tensor from torch.unfold() is a view (zero-copy) of the original waveform, so it adds no memory. Only the per-batch slice (batch_size x 1 x window_samples) is ever materialized.

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Output Accuracy

Speaker counts match exactly across all files on both CUDA and MPS (±1 on high-speaker files due to VBx clustering non-determinism). Segment counts are within ±1.8%, also attributable to VBx non-determinism rather than inference accuracy differences.

CUDA Accuracy

Audio Length	Stock Speakers	Opt Speakers	Stock Segments	Opt Segments
0.5h	4	4	597	596
1.0h	5	5	1,179	1,184
2.2h	3	3	2,593	2,640
3.2h	3	3	2,687	2,687
4.7h	21	22	11,606	11,614

MPS Accuracy

Audio Length	Stock Speakers	Opt Speakers	Stock Segments	Opt Segments
0.5h	5	4	617	632
1.0h	5	5	1,321	1,324
2.2h	3	3	2,851	2,650
3.2h	3	3	2,902	2,905
4.7h	8	8	9,693	9,343

Note: MPS stock shows 5 speakers on the 0.5h file while optimized shows 4. Analysis reveals the extra speaker is a 11.1s fragment (0.6% of audio, 4 segments) that stock splits off — a VBx non-determinism artifact, not an accuracy difference.

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Changes (4 files)

1. Vectorized chunk extraction (speaker_diarization.py) — CUDA + MPS + CPU

Before: iter_waveform_and_mask() calls self._audio.crop(file, chunk) individually for every chunk — tens of thousands of Python-level crop operations.

After: A single torch.unfold() extracts all overlapping audio chunks in one vectorized operation. This is device-agnostic and benefits all backends.

# Before: N individual crop() calls in a generator
for chunk, masks in binary_segmentations:
    waveform, _ = self._audio.crop(file, chunk, mode="pad")
    ...

# After: one vectorized operation
all_chunks = waveform.unfold(1, window_samples, step_samples).squeeze(0)

2. Memory-efficient batch indexing (speaker_diarization.py) — CUDA + MPS + CPU

Before: repeat_interleave() pre-materializes all chunk x speaker combinations — 58.8GB for a 4.7h/21-speaker file.

After: Per-batch indexing into all_chunks using integer division — constant 39MB regardless of file length or speaker count.

# Before: materializes everything (OOM on low-RAM machines)
flat_waveforms = all_chunks.repeat_interleave(num_speakers, dim=0).unsqueeze(1)
waveform_batch = flat_waveforms[start:end]

# After: constant memory per batch
def _get_batch_waveforms(start, end):
    chunk_indices = torch.arange(start, end) // num_speakers
    return all_chunks[chunk_indices].unsqueeze(1)

3. Vectorized mask selection (speaker_diarization.py) — CUDA + MPS + CPU

Before: Per-chunk, per-speaker Python loop selecting between clean and regular masks.

After: NumPy broadcasting selects all masks at once. Also device-agnostic.

# Before: nested loop with conditionals
for mask, clean_mask in zip(masks.T, clean_masks.T):
    if np.sum(clean_mask) > min_num_frames:
        used_mask = clean_mask
    else:
        used_mask = mask

# After: vectorized selection
clean_sums = np.sum(clean_data, axis=1)
use_clean = clean_sums > min_num_frames
final_masks = np.where(use_clean[:, :, np.newaxis], clean_transposed, binary_transposed)

4. TF32 re-enablement for embedding inference (speaker_diarization.py) — CUDA only

fix_reproducibility() disables TF32 during segmentation for deterministic results. This PR re-enables TF32 after segmentation completes, before the embedding loop. TF32 provides ~15% speedup on Ampere+ GPUs (RTX 3000+, A-series, RTX 4000+). On pre-Ampere GPUs, the flag is silently ignored.

# Re-enable after segmentation, before embedding loop
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True

5. Adaptive batch size selection (speaker_diarization.py) — CUDA + MPS

Auto-selects embedding batch size (64-256) based on available GPU memory, instead of relying on a hardcoded value.

• CUDA: Queries torch.cuda.get_device_properties().total_mem and torch.cuda.memory_reserved() for actual free VRAM
• MPS: Queries torch.mps.recommended_max_memory() (returns ~2/3 of total RAM, the safe GPU allocation limit). Falls back to os.sysconf total RAM query, then 8GB safe default. No hardcoded memory assumptions — works correctly on machines from 8GB to 192GB unified memory.

# CUDA: query discrete VRAM
free_vram_mb = (total_mem - reserved_mem) / (1024 * 1024)

# MPS: query actual system limits (no hardcoded budget)
if hasattr(torch.mps, "recommended_max_memory"):
    budget_mb = torch.mps.recommended_max_memory() / (1024 * 1024)
else:
    total_ram = os.sysconf("SC_PHYS_PAGES") * os.sysconf("SC_PAGE_SIZE")
    budget_mb = total_ram * 0.75 / (1024 * 1024)
free_vram_mb = max(0, budget_mb - allocated)

# Both: same selection logic
auto_bs = max(64, min(256, int(free_vram_mb * 0.4 / 60)))
auto_bs = 2 ** int(math.log2(auto_bs))  # Round to power of 2

Only activates when embedding_batch_size <= 32 (the typical default range), so explicit user-configured values are respected.

6. Double-buffered CUDA stream prefetch (speaker_diarization.py) — CUDA only

While the GPU processes embedding batch N, a separate CUDA stream transfers batch N+1 from CPU to GPU using pinned memory and non-blocking transfers. This hides H2D transfer latency. Not applicable to MPS (unified memory architecture eliminates CPU↔GPU transfers).

transfer_stream = torch.cuda.Stream(device=device)

def prefetch_waveforms(idx):
    with torch.cuda.stream(transfer_stream):
        wf = _get_batch_waveforms(s, e).pin_memory().to(device, non_blocking=True)
        mk = flat_masks_tensor[s:e].pin_memory().to(device, non_blocking=True)
    return wf, mk

7. Direct model calls bypassing wrapper overhead (speaker_diarization.py) — CUDA + MPS

Calls model_.compute_fbank() + model_.resnet() directly instead of through PyannoteAudioPretrainedSpeakerEmbedding.__call__(), avoiding redundant pin_memory, mask resampling, and other per-call overhead. Works on both CUDA and MPS.

8. MPS-native FFT for fbank computation (wespeaker/__init__.py) — MPS

Before: Stock code unconditionally falls back to CPU for FFT on MPS devices (fft_device = torch.device("cpu") if device.type == "mps"). This was necessary when MPS FFT was broken (pre-PyTorch 2.3), but creates a per-batch CPU↔MPS round-trip that dominates embedding time.

After: Attempts FFT directly on MPS first (works in PyTorch 2.3+). Falls back to CPU only if MPS FFT raises RuntimeError (older PyTorch).

# Before: always falls back to CPU (unnecessary in PyTorch 2.3+)
fft_device = torch.device("cpu") if device.type == "mps" else device
features = torch.vmap(self._fbank)(waveforms.to(fft_device)).to(device)

# After: use MPS FFT when available, CPU fallback for older PyTorch
if device.type == "mps":
    try:
        features = torch.vmap(self._fbank)(waveforms)  # MPS FFT (PyTorch 2.3+)
    except RuntimeError:
        features = torch.vmap(self._fbank)(waveforms.cpu()).to(device)  # CPU fallback
else:
    features = torch.vmap(self._fbank)(waveforms)

Profiling data (Mac Studio M2 Max, batch=32): MPS FFT is 4.46x faster than the CPU fallback (13.1ms vs 58.6ms). This eliminates the single largest MPS bottleneck.

9. GPU memory cleanup between pipeline stages (speaker_diarization.py) — CUDA + MPS

Calls torch.cuda.empty_cache() or torch.mps.empty_cache() (as appropriate) after segmentation and after embedding extraction. This releases cached allocations before the next stage, preventing memory accumulation. Profiling confirms empty_cache() is essentially free on both devices (mean 0.009ms on MPS, <0.1ms on CUDA).

10. Pinned memory for segmentation and embedding transfers (inference.py, speaker_verification.py) — CUDA only

Uses pin_memory().to(device, non_blocking=True) for CPU-to-GPU tensor transfers in both the segmentation model inference and the embedding model forward pass. Only activates on CUDA devices. MPS uses unified memory (no CPU↔GPU boundary), so pinned memory is not applicable and is correctly skipped.

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Device Support Matrix

Optimization	CUDA	MPS	CPU
Vectorized chunk extraction	Yes	Yes	Yes
Memory-efficient batch indexing	Yes	Yes	Yes
Vectorized mask selection	Yes	Yes	Yes
TF32 re-enablement	Yes	N/A	N/A
Adaptive batch size	Yes	Yes	—
CUDA stream prefetch	Yes	N/A	N/A
Direct model calls	Yes	Yes	—
MPS-native FFT	N/A	Yes	N/A
Memory cleanup (empty_cache)	Yes	Yes	—
Pinned memory transfers	Yes	N/A	N/A
Waveform release post-embedding	Yes	Yes	Yes

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MPS Profiling Data

Detailed profiling on Mac Studio M2 Max (32GB) reveals MPS-specific characteristics:

Operation Costs

Operation	Mean	Notes
torch.mps.empty_cache()	0.009ms	Effectively free
torch.mps.synchronize()	0.000ms	No-op (unified memory)
CPU→MPS transfer (100MB)	2.4ms (40.8 GB/s)	Unified memory — pointer remap, not DMA
torch.unfold() (1890 chunks)	0.0ms	Zero-copy view

MPS FFT vs CPU FFT (fbank computation)

Batch Size	MPS FFT	CPU FFT	MPS Speedup
16	50.4ms	29.5ms	0.58x (CPU faster)
32	13.1ms	58.6ms	4.46x
64	30.2ms	109.3ms	3.61x
128	48.4ms	201.2ms	4.16x

MPS FFT has a dispatch overhead that amortizes at batch >= 32. Since the split pipeline uses auto-selected batches of 64+, the MPS FFT path is always faster in practice.

Embedding Forward Pass Scaling

Batch Size	Total	Per-chunk	Notes
16	127.7ms	7.98ms
32	234.1ms	7.31ms	Best per-chunk efficiency
64	473.2ms	7.39ms
128	1029.5ms	8.04ms

Per-chunk cost is flat on MPS (minimal GPU parallelism for this model size). Batch 32 has the best per-chunk time, validating the adaptive batch sizing approach.

Segmentation Model Scaling

Batch Size	Total	Per-chunk
1	53.1ms	53.05ms
4	118.0ms	29.51ms
16	95.5ms	5.97ms
32	78.3ms	2.45ms

Segmentation model benefits significantly from batching on MPS — 21x faster per-chunk at batch=32 vs batch=1.

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Backward Compatibility

• CPU devices: Vectorized chunk extraction, mask selection, and memory-efficient indexing still apply (device-agnostic). The optimized split pipeline falls back to the original sequential embedding loop.
• MPS devices: All applicable optimizations are gated behind device.type == "mps" checks. Features unsupported on MPS (pin_memory, CUDA streams, TF32) are correctly skipped. FFT fix includes try/except fallback for older PyTorch where MPS FFT is broken.
• Low-memory machines (8-16GB): Memory-efficient batch indexing prevents OOM. Adaptive batch sizing queries actual system memory limits (no hardcoded assumptions). Safe fallback to batch_size=64 on exception.
• Pre-Ampere CUDA GPUs: TF32 flags are silently ignored. Adaptive batch sizing works on any CUDA GPU (tested conceptually down to 6GB VRAM).
• Existing API: No changes to public API. embedding_batch_size, segmentation_batch_size, and all pipeline parameters work as before.
• Fallback path: When the optimized pipeline conditions aren't met (CPU device, missing compute_fbank/resnet methods), the code falls back to the original sequential embedding loop with no behavior change.

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Related Issues

• Performance Bug when calculating speaker embeddings #1614 — Manual embedding extraction 8s vs pipeline 240s (30x overhead)
• Very low GPU usage(5%) and slow diarization #1403 — 5% GPU, 100% CPU during diarization
• High CPU usage during embeddings step of diarization #1753 — 100% on 32 CPU cores, GPU underutilized during embedding
• Pyannote is 10 times slower than WhisperX with GPU utilization 10%: expected behavior or misconfiguration? #1652 — 10x slower than WhisperX at 10% GPU utilization
• Inconsistent Inference Speed vs GPU Utilization with Batch Size Variation in pyannote Audio Models #1566 — Batch sizes 32/64/128 all ~70s, confirming CPU-side bottleneck

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How to Test

# CUDA
from pyannote.audio import Pipeline
import torch

pipeline = Pipeline.from_pretrained(
    "pyannote/speaker-diarization-3.1", token="YOUR_TOKEN"
)
pipeline = pipeline.to(torch.device("cuda"))
pipeline.embedding_batch_size = 32  # Auto-elevates to 64-256 based on VRAM
output = pipeline({"waveform": waveform, "sample_rate": 16000})

# MPS (Apple Silicon)
pipeline = pipeline.to(torch.device("mps"))
pipeline.embedding_batch_size = 32  # Auto-selects based on unified memory
output = pipeline({"waveform": waveform, "sample_rate": 16000})

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Test Hardware

Platform	Device	Memory	PyTorch	Python
CUDA	NVIDIA RTX A6000	48GB VRAM	2.8.0+cu128	3.12
MPS	Apple M2 Max (Mac Studio)	32GB unified	2.10.0	3.13

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[Karen86Tonoyan]

KAREN TONOYAN