[whisper] Add Flash Attention and batched decoding for up to 10x speedup

Summary

Added Flash Attention and batched segment decoding to mlx-whisper, achieving 9.5x speedup on Apple Silicon.

Changes

1. Flash Attention (whisper.py)

• Replaced manual QKV attention with mx.fast.scaled_dot_product_attention
• Conditional path: uses flash attention by default, falls back to standard attention when word_timestamps=True (needs QK weights for DTW alignment)
• Proper mask handling for autoregressive decoding with KV cache

2. Batched decoding (transcribe.py)

• New batch_size parameter in transcribe() (default=1, fully backward-compatible)
• Pre-slices audio into fixed 30s chunks, stacks into batch tensor (N, 3000, n_mels), decodes simultaneously
• Per-segment temperature fallback for quality control
• batch_size=1 produces identical output to current code

Zero new dependencies. No breaking changes.

Benchmarks (M2 8GB, whisper-small, 5 min Russian audio)

For a 15-hour video: ~20 minutes instead of ~3 hours.

Code

Full implementation with benchmarks: https://github.com/ilyasmukiev/mlx-whisper-pr

• Branch flash-attention-batch: minimal changes (Flash Attention + batch_size parameter only)
• Branch full-batching-vad-diarize: adds optional VAD (Silero) and speaker diarization

Standalone package: https://github.com/ilyasmukiev/mlx-whisper-fast

Notes

• Could not create a PR directly because gh repo fork returns HTTP 502 (repo too large?)
• Happy to submit a proper PR once the fork works
• The batched path uses fixed-stride chunking (no dynamic seeking), which is a deliberate trade-off for parallelism — same approach as WhisperX and lightning-whisper-mlx
• Related: Discussion #1275 where batching was acknowledged as possible but not implemented

Start by reviewing whisper.py and transcribe.py, then compare the proposed flash-attention and batched-decoding implementation in the linked mlx-whisper-pr repository. Confirm the fallback behavior, batch_size backward compatibility, identical batch_size=1 output, and the reported benchmarks before defining the final tests and acceptance criteria.