wishcribe 1.4.1

Fast multi-speaker audio/video transcription — faster-whisper + pyannote.audio

Wishcribe

Fast multi-speaker audio/video transcription — fully offline after first run.

faster-whisper + pyannote.audio + native Apple Silicon support (MLX-Whisper).

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Features

• Multi-speaker transcription — speaker labels per segment ([SPEAKER_00], [SPEAKER_01], …)
• 4–8× faster than openai-whisper via CTranslate2 batched inference + VAD
• Apple Silicon native — MLX-Whisper auto-selected on M1/M2/M3/M4 (Neural Engine / GPU), chip name shown in banner
• Fully offline after one-time model download
• Word-level timestamps — --word-timestamps embeds per-word timing in SRT/JSON
• VAD controls — --no-vad, --vad-threshold, --vad-min-silence-ms, --vad-speech-pad-ms
• Silence suppression — --no-speech-threshold discards hallucinated segments in quiet regions
• Fast mode — --fast-mode enables greedy decoding + VAD chunk packing + concurrent model warmup on M1 (~40-50% faster)
• Auto-diarize fallback — warns and continues without speaker labels if no token and no cached model
• Accuracy controls — --initial-prompt, --temperature, --beam-size
• Video + audio — mp4, mkv, mov, avi, webm, ts, wmv, flv, mp3, wav, m4a, flac, ogg, aac, opus, wma
• Multiple output formats — .txt, .srt, .json
• Python API + CLI

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Installation

pip install wishcribe

Apple Silicon (M1/M2/M3/M4) — fastest

pip install "wishcribe[apple]"

Installs mlx-whisper for Neural Engine / GPU acceleration. Automatically selected when running on Apple Silicon.

Legacy fallback

pip install "wishcribe[legacy]"   # openai-whisper (slower, no CTranslate2 needed)
pip install "wishcribe[api]"      # OpenAI cloud API (no local GPU needed)

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Quick start

Step 1 — one-time model download

wishcribe download --hf-token hf_xxxxxxxxxx

This downloads and caches all required models:

• Whisper model (default: large-v2 on non-Apple, turbo on Apple Silicon)
• pyannote speaker diarization model (~1 GB)

After this, wishcribe works fully offline forever.

HuggingFace setup checklist (first time only):

1. Sign up → https://huggingface.co/join
2. Accept model license → https://huggingface.co/pyannote/speaker-diarization-community-1
3. Create a Read token → https://huggingface.co/settings/tokens

Step 2 — transcribe

wishcribe --video meeting.mp4

Output files are saved next to the input: meeting_transcript.txt and meeting_transcript.srt.

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CLI reference

wishcribe download

wishcribe download [OPTIONS]

Option	Description
--hf-token TOKEN	HuggingFace read token (required on first run)
--model MODEL	Whisper model to download (default: large-v2)
--force	Delete existing cache and re-download

wishcribe / wishcribe run

wishcribe --video FILE [OPTIONS]
wishcribe run --video FILE [OPTIONS]

Input:

Option	Description
--video PATH	Path to video or audio file (required)
--bahasa LANG	Language code: id, en, zh, etc. (default: auto-detect)

Whisper model:

Option	Description
--model MODEL	tiny | base | small | medium | large | large-v1 | large-v2 | large-v3 | turbo (default: large-v2)

Accuracy:

Option	Description
--initial-prompt TEXT	Domain context to guide transcription vocabulary and style. Disables batched inference.
--temperature FLOAT	Sampling temperature (default: 0.0 = greedy). Non-zero disables batched inference.
--beam-size N	Beam search width (default: 5). Only used when batched inference is disabled.
--word-timestamps	Embed word-level timing in SRT (one block per word) and JSON (words array). Not supported by MLX-Whisper.
--no-speech-threshold FLOAT	Discard segments below this non-speech probability (default: 0.6). Suppresses hallucinations in silent regions.
--fast-mode	Enable M1-optimised fast transcription: greedy decoding (beam_size=1), VAD chunk packing, concurrent model warmup. ~40-50% faster on Apple Silicon. MLX-Whisper only.

VAD (Voice Activity Detection):

Option	Description
--no-vad	Disable VAD. Use when VAD incorrectly trims real speech.
--vad-threshold FLOAT	VAD speech probability threshold (default: 0.5).
--vad-min-silence-ms MS	Minimum silence gap in ms to split chunks (default: 500).
--vad-speech-pad-ms MS	Padding added around detected speech in ms (default: 200).

Speed / hardware:

Option	Description
--batch-size N	Batch size (default: 16). Lower to 4–8 on OOM.
--compute-type TYPE	float16 (GPU, fast) | int8 (CPU/low-mem) | float32 (CPU, accurate)
--device DEVICE	cuda | cpu (default: auto-detect)

Speaker diarization:

Option	Description
--hf-token TOKEN	HuggingFace token (or set WISHCRIBE_HF_TOKEN env var)
--model-path PATH	Local pyannote model folder (skips HuggingFace)
--speakers N	Number of speakers — improves accuracy when known
--no-diarize	Skip speaker diarization (no token needed, faster)

Output:

Option	Description
--output DIR	Output folder (default: same as input file)
--json	Also save .json transcript
--no-txt	Skip .txt output
--no-srt	Skip .srt output
--quiet	Suppress progress output

Cloud API:

Option	Description
--use-api	Use OpenAI Whisper API instead of local model
--api-key KEY	OpenAI API key (required with --use-api)

---

Examples

# Indonesian meeting with 3 speakers
wishcribe --video rapat.mp4 --bahasa id --speakers 3

# English podcast, no speaker labels needed
wishcribe --video podcast.mp3 --no-diarize

# Medical dictation — guide vocabulary with a prompt
wishcribe --video dictation.mp4 --initial-prompt "Medical: hypertension, tachycardia, bradycardia."

# Word-level timestamps (karaoke-style SRT)
wishcribe --video interview.mp4 --word-timestamps

# Apple Silicon fast mode — ~40-50% faster, greedy decoding (v1.4.0)
wishcribe --video meeting.mp4 --fast-mode

# Suppress silence hallucinations more aggressively
wishcribe --video lecture.mp4 --no-speech-threshold 0.8

# Disable VAD if it trims real speech
wishcribe --video quiet_speaker.mp4 --no-vad

# Tune VAD sensitivity
wishcribe --video meeting.mp4 --vad-threshold 0.3 --vad-min-silence-ms 300

# Higher accuracy — beam search width 10
wishcribe --video interview.mp4 --beam-size 10

# Low-memory CPU-only mode
wishcribe --video lecture.mp4 --device cpu --compute-type int8 --batch-size 4

# Save JSON output too
wishcribe --video meeting.mp4 --json

# Re-download turbo model (e.g. after switching models)
wishcribe download --model turbo --force

# Specify HF token via environment variable (no --hf-token flag needed)
export WISHCRIBE_HF_TOKEN=hf_xxxxxxxxxx
wishcribe --video meeting.mp4

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Python API

from wishcribe import transcribe, download

# One-time setup
download(hf_token="hf_xxxxxxxxxx")

# Basic transcription (saves .txt and .srt next to the input file)
segments = transcribe("meeting.mp4")

# With options
segments = transcribe(
    "meeting.mp4",
    model="large-v2",       # or "turbo" — auto-selected on Apple Silicon
    language="id",           # BCP-47 language code; None = auto-detect
    num_speakers=2,          # known speaker count improves diarization
    output_dir="./output",   # where to save files
    save_json=True,          # also write .json
)

# Skip diarization (no token needed)
segments = transcribe("lecture.mp4", diarize=False)

# Word-level timestamps in SRT and JSON
segments = transcribe("interview.mp4", word_timestamps=True)

# Suppress silence hallucinations
segments = transcribe("lecture.mp4", no_speech_threshold=0.8)

# Fast mode — ~40-50% faster on Apple Silicon M1 (v1.4.0)
# Greedy decoding + VAD chunk packing + concurrent model warmup
segments = transcribe("meeting.mp4", fast_mode=True)

# Disable VAD if it trims real speech
segments = transcribe("quiet.mp4", vad_filter=False)

# Tune VAD
segments = transcribe(
    "meeting.mp4",
    vad_threshold=0.3,
    vad_min_silence_ms=300,
    vad_speech_pad_ms=150,
)

# Accuracy controls
segments = transcribe(
    "dictation.mp4",
    initial_prompt="Medical: hypertension, tachycardia.",
    beam_size=10,
)

# Speed controls
segments = transcribe(
    "meeting.mp4",
    batch_size=8,
    compute_type="int8",
    device="cpu",
)

# Each segment has: .start  .end  .speaker  .text  .words (when word_timestamps=True)
for seg in segments:
    print(f"[{seg.speaker}] {seg.start:.1f}s -> {seg.end:.1f}s  {seg.text}")

# HF token via environment variable
import os
os.environ["WISHCRIBE_HF_TOKEN"] = "hf_xxxxxxxxxx"
segments = transcribe("meeting.mp4")  # token picked up automatically

---

Model guide

Model	Size	Speed	Accuracy	Notes
tiny	75 MB	⚡⚡⚡⚡	★★	Fastest; fair accuracy
base	139 MB	⚡⚡⚡	★★★	Good for clear speech
small	461 MB	⚡⚡⚡	★★★	Better accuracy
medium	1.4 GB	⚡⚡	★★★★	Good speed/accuracy balance
turbo	1.6 GB	⚡⚡⚡	★★★★	Default on Apple Silicon ⭐
large-v1	2.9 GB	⚡	★★★★	Original large model
large-v2	2.9 GB	⚡	★★★★★	Default on non-Apple ⭐
large-v3	3.1 GB	⚡	★★★★★	Newest large model

On Apple Silicon, turbo is the default — it uses the Neural Engine and is significantly faster than large-v2 with only a marginal accuracy difference.

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Backend selection

wishcribe automatically picks the best available backend:

Apple Silicon (M1/M2/M3/M4)
└── mlx-whisper installed?  → MLX-Whisper (Neural Engine / GPU)
└── else                    → faster-whisper (CPU)

Other platforms
└── faster-whisper installed?  → faster-whisper + batched inference + VAD
└── else                       → openai-whisper (fallback, slower)

MLX-Whisper (Apple Silicon)

Automatically selects the right quantized model based on available RAM:

RAM	Model loaded
≥ 16 GB	Full precision
≥ 8 GB	4-bit quantized
< 8 GB	8-bit quantized

---

Accuracy tips

--initial-prompt injects a text hint before transcription. Use it to:

• Improve domain-specific vocabulary (medical, legal, tech)
• Set consistent casing and punctuation style
• Guide the model when it struggles with acronyms or names

wishcribe --video standup.mp4 --initial-prompt "Engineering: Kubernetes, CI/CD, pull request, deployment."

Note: --initial-prompt and non-zero --temperature disable batched inference and fall back to standard beam search. This is slower but gives more control.

--beam-size (default: 5) controls beam search width in the non-batched path. Increasing to 8–10 improves accuracy slightly at the cost of speed.

--temperature (default: 0.0 = greedy) adds sampling randomness. Useful when the model gets stuck in repetitive output. Try 0.1–0.2.

--no-speech-threshold (default: 0.6) discards segments where the model is uncertain whether the audio contains speech. Increase toward 1.0 to suppress more aggressively in recordings with long silences.

--no-vad disables Voice Activity Detection. Use this if VAD incorrectly cuts off quiet speakers, music beds, or recordings with ambient noise.

--fast-mode (v1.4.0, Apple Silicon M1 only) activates three stacked optimisations:

• Greedy decoding (beam_size=1, best_of=1) — eliminates beam search, ~40-50% faster on M1. M1's GPU handles parallel beam candidates less efficiently than CUDA, so the gain is larger than on NVIDIA hardware.
• VAD chunk packing — speech segments are greedily packed into 30 s windows before sending to the model. Reduces the number of MLX transcribe() calls and maximises GPU occupancy per call.
• Concurrent model warmup — the MLX model starts loading in a background thread immediately, overlapping with the VAD preprocessing step. On M1 unified memory there is no PCIe copy, so this parallelism is nearly free.

All three are best-effort — any failure falls back to standard full-file transcription silently.

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Output format

.txt

[SPEAKER_00] 0:00:01
  Good morning everyone, let's get started.

[SPEAKER_01] 0:00:05
  Thanks for joining. Today we'll cover the Q3 results.

.srt (segment-level, default)

1
00:00:01,000 --> 00:00:04,500
[SPEAKER_00] Good morning everyone, let's get started.

2
00:00:05,200 --> 00:00:09,800
[SPEAKER_01] Thanks for joining. Today we'll cover the Q3 results.

.srt (word-level, with --word-timestamps)

1
00:00:01,000 --> 00:00:01,380
[SPEAKER_00] Good

2
00:00:01,420 --> 00:00:01,710
morning

3
00:00:01,740 --> 00:00:02,100
everyone,

.json

[
  {
    "start": 1.0,
    "end": 4.5,
    "duration": 3.5,
    "speaker": "SPEAKER_00",
    "text": "Good morning everyone, let's get started.",
    "words": [
      {"word": "Good", "start": 1.0, "end": 1.38, "probability": 0.99},
      {"word": "morning", "start": 1.42, "end": 1.71, "probability": 0.98}
    ]
  }
]

words is only present when --word-timestamps is used. duration is always included.

---

Environment variables

Variable	Description
WISHCRIBE_HF_TOKEN	HuggingFace token — avoids passing --hf-token every time
HF_TOKEN	Fallback if WISHCRIBE_HF_TOKEN is not set
OMP_NUM_THREADS	CPU thread count — auto-tuned to performance cores on Apple Silicon

Add to ~/.zshrc or ~/.bashrc:

export WISHCRIBE_HF_TOKEN="hf_xxxxxxxxxx"

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Troubleshooting

Whisper model large-v2 is not cached

wishcribe download --model large-v2
# or force a clean re-download:
wishcribe download --model large-v2 --force

Diarization model not found in local cache

wishcribe download --hf-token hf_xxxxxxxxxx

Make sure you've accepted the license at https://huggingface.co/pyannote/speaker-diarization-community-1

Diarization auto-skipped with warning

If no token and no cached model, wishcribe now continues with transcription only (no speaker labels) instead of crashing. To enable speaker labels:

export WISHCRIBE_HF_TOKEN=hf_xxxxxxxxxx
wishcribe --video meeting.mp4

CUDA out of memory

wishcribe --video meeting.mp4 --batch-size 4
# or CPU mode:
wishcribe --video meeting.mp4 --device cpu --compute-type int8

VAD cutting off speech

# Disable VAD entirely
wishcribe --video meeting.mp4 --no-vad
# Or lower the threshold (more sensitive)
wishcribe --video meeting.mp4 --vad-threshold 0.3

Hallucinated text in silent regions

# Increase no-speech threshold
wishcribe --video meeting.mp4 --no-speech-threshold 0.8

mlx-whisper not installed (Apple Silicon)

pip install mlx-whisper
# or reinstall with apple extra:
pip install "wishcribe[apple]"

Fast mode not speeding things up

# Verify mlx-whisper is installed (fast_mode only affects the MLX path)
pip install "wishcribe[apple]"
# Try on a longer file — warmup overhead is proportionally smaller
wishcribe --video long_meeting.mp4 --fast-mode

Inaccurate transcription with --fast-mode

# fast_mode uses greedy decoding — drop it for beam search accuracy
wishcribe --video meeting.mp4   # standard mode, beam_size=5

Inaccurate transcription

# Add domain context
wishcribe --video meeting.mp4 --initial-prompt "Context: ..."
# Increase beam search
wishcribe --video meeting.mp4 --beam-size 10
# Specify language (skip auto-detect)
wishcribe --video meeting.mp4 --bahasa id

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Supported formats

Video: mp4, mkv, mov, avi, webm, ts, wmv, flv

Audio: mp3, wav, m4a, flac, ogg, aac, opus, wma

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Requirements

• Python 3.9+
• moviepy >= 2.0 + ffmpeg (audio extraction)
• faster-whisper >= 1.0 (transcription)
• pyannote.audio >= 3.1 (diarization)
• torch >= 2.0
• Apple Silicon only: mlx-whisper >= 0.4, soundfile >= 0.12 (pip install "wishcribe[apple]")

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Changelog

v1.4.0

• --fast-mode — three stacked M1 speed optimisations on MLX-Whisper (Apple Silicon only):
  • Greedy decoding (beam_size=1, best_of=1): ~40-50% faster on M1 vs default beam search
  • VAD chunk packing: greedy 30 s window packing maximises GPU occupancy per MLX call
  • Concurrent model warmup: MLX model loads in background thread while Silero VAD runs on CPU
  • All three are best-effort — any failure falls through to standard full-file path silently
• soundfile>=0.12.0 added to [apple] optional extra (required for VAD chunk packing)

v1.3.2

• Reverted Lightning-Whisper-MLX backend (incompatible with current Apple Silicon environment)
• MLX-Whisper remains the sole Apple Silicon backend (priority 1 on M-series)
• All v1.3.0 features intact: word timestamps, VAD controls, no-speech threshold, auto-diarize fallback

v1.3.0

• Word-level timestamps — --word-timestamps embeds per-word timing in SRT (karaoke-style, one block per word) and JSON (words array with start, end, probability)
• No-speech threshold — --no-speech-threshold (default 0.6) discards hallucinated segments in silent regions; applied to both batched and standard transcription paths
• VAD escape hatch — --no-vad disables Voice Activity Detection for recordings where VAD incorrectly trims real speech
• VAD sensitivity controls — --vad-threshold, --vad-min-silence-ms, --vad-speech-pad-ms
• Cross-window coherence fix — condition_on_previous_text restored to True (was incorrectly set to False in v1.1.1); hallucination now suppressed via no_speech_threshold instead
• Apple Silicon chip name in banner — shows exact chip label (e.g. Apple M3 Pro) via sysctl
• Auto-diarize fallback — warns and continues without speaker labels instead of crashing when no token and no cached model
• AVFFrameReceiver / objc[] warning suppression — extended to pyannote diarization (onnxruntime fires these on every runSession call)
• Actionable model.bin error — clear message with exact --force command when Whisper cache is incomplete

v1.2.1

• Fix: _apple_perf_cores function definition restored (was accidentally removed, caused NameError on Apple Silicon)
• Fix: no_speech_threshold now applied in batched inference path (was only in standard path)
• Fix: write_txt and write_srt write empty file (not bare newline) for empty segment lists
• Fix: Progress dot no longer printed for blank/empty segments
• Fix: PEP 8 blank line between _suppress_fd2 and module-level constants in diarize.py

v1.2.0

• MLX-Whisper backend — Apple Silicon M1/M2/M3/M4, auto-selected when mlx-whisper is installed
• Default model turbo on Apple Silicon (faster + accurate via Neural Engine)
• OMP_NUM_THREADS auto-tuned to physical performance cores on Apple Silicon
• Auto-quantization — picks 4-bit or 8-bit MLX model based on available unified memory
• --initial-prompt — inject domain context for specialised vocabulary
• --temperature — sampling temperature (0.0 = true greedy, always deterministic)
• --beam-size — beam search width for non-batched path
• Pipeline memory leak fix — GPU memory freed even when BatchedInferencePipeline.transcribe() raises mid-batch
• Diarization memory fix — GPU/MPS memory freed even if segment extraction fails
• wishcribe download mirrors transcription default on Apple Silicon (turbo, not large-v2)
• large-v1 added to CLI model choices
• MLX cache purge on --force re-download
• moviepy>=2.0.0 pin (2.x API required)

v1.1.1 (bug fixes, included in v1.2.0)

• Fix: _suppress_fd2() degrades gracefully on unusual fd environments
• Fix: sys.exit() replaced with RuntimeError throughout (library-safe)
• Fix: GPU memory freed after transcription before diarization loads

v1.1.0

• faster-whisper backend (4–8x speedup over openai-whisper)
• Batched inference + VAD (silence filtering)
• float16/int8 auto-selection by device
• wishcribe download pre-caches all models

v1.0.0

• Initial release (openai-whisper + pyannote.audio)

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License

MIT — see LICENSE

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Links

• PyPI: https://pypi.org/project/wishcribe/
• GitHub: https://github.com/dwicak/wishcribe
• Issues: https://github.com/dwicak/wishcribe/issues