py-roller 0.5.7

Composable lyric-audio alignment pipeline with staged execution, batch processing, and LRC/ASS export.

py-roller

py-roller is a local command-line pipeline for generating rolling lyric files from audio and plain-text lyrics.

It can split vocals, filter audio, transcribe local audio with faster-whisper or wav2vec2-style backends, parse lyrics, align lyric lines, and export LRC or ASS karaoke output.

• Package name: py-roller
• CLI command: py-roller
• Python import package: pyroller

Install

Use a fresh virtual environment when possible. First install the lightweight base package from source:

pip install -e .

Then install the validated audio/transcriber runtime:

py-roller install
py-roller doctor

py-roller install installs a pinned Torch/Torchaudio/TorchVision profile first, then installs the bundled audio-core requirements with matching constraints, validates the environment, and runs py-roller doctor unless --skip-doctor is passed.

Install profiles:

• auto default: try the best validated profile for this machine, then fall back to CPU if validation fails.
• cpu: force the CPU-only profile.
• cu124: force the CUDA 12.4 profile.

Useful install commands:

py-roller install --profile cpu
py-roller install --profile cu124
py-roller install --dry-run
py-roller install --no-reset-torch
py-roller doctor --output-format json
py-roller install --progress-format jsonl --output-format json

Machine-readable runtime checks and install progress

doctor can print a machine-readable report for GUI frontends and automated runtime checks:

py-roller doctor --output-format json

The JSON report includes ok, the Python executable and platform, one entry per runtime check, and a suggested_next_step when the environment needs repair. The default remains the terminal checklist:

py-roller doctor --output-format human

install now separates live progress from the final summary:

py-roller install --progress-format human --output-format human   # default
py-roller install --progress-format jsonl --output-format json    # GUI-friendly
py-roller install --progress-format both --output-format json     # debug both streams

--progress-format jsonl emits PYROLLER_EVENT lines for install lifecycle events, selected profiles, subprocess starts/completions, subprocess output, validation, doctor, completion, failures, and heartbeat messages when a pip subprocess is still running without output. --output-format json prints a final install report containing the requested profile, selected profile, step results, validation results, and doctor summary when doctor is run.

Quick start

Set --language explicitly whenever the song language is known. Use zh for Chinese, en for English, and mul only when you need the multilingual fallback.

Raw audio + lyrics -> LRC

py-roller run \
  --stages s,f,t,p,a,w \
  --audio ./song.mp3 \
  --lyrics ./song.txt \
  --language zh \
  --filter-chain noise_gate,dereverb \
  --output-roller ./song.lrc

Prepared vocal track + lyrics -> ASS karaoke

py-roller run \
  --stages t,p,a,w \
  --audio ./vocals.wav \
  --lyrics ./song.txt \
  --language zh \
  --writer-backend ass_karaoke \
  --output-roller ./song.ass

Batch process directories by filename stem

py-roller batch \
  --stages t,p,a,w \
  --audio ./audio_dir \
  --lyrics ./lyrics_dir \
  --language zh \
  --output-roller ./out_dir

Pipeline model

py-roller runs a contiguous chain of stages in this fixed order:

s -> f -> t -> p -> a -> w
splitter -> filter -> transcriber -> parser -> aligner -> writer

Valid examples:

• s,f,t,p,a,w: full pipeline from raw audio and lyrics.
• t,p,a,w: start from prepared vocal/filtered audio.
• a,w: start from existing timed_units and parsed_lyrics artifacts.
• w: rewrite from an existing alignment_result artifact.

Invalid examples:

• s,t,w: skips required intermediate stages.
• s,p,a: skips required intermediate stages.

Legal chain-start inputs:

• --audio: valid when the chain starts at s, f, or t.
• --lyrics: required when the chain includes p.
• --timed-units and --parsed-lyrics: valid when the chain starts at a.
• --alignment-result: valid when the chain starts at w.

Final outputs are only the explicit --output-* paths:

• --output-vocal-audio
• --output-filtered-audio
• --output-timed-units
• --output-parsed-lyrics
• --output-alignment-result
• --output-roller

Intermediate files under --intermediate are temporary working state unless --cleanup never is used.

Common workflows

Start from raw audio

py-roller run \
  --stages s,f,t,p,a,w \
  --audio ./song.mp3 \
  --lyrics ./song.txt \
  --language zh \
  --output-roller ./song.lrc

Start from already-separated vocals

py-roller run \
  --stages t,p,a,w \
  --audio ./vocals.wav \
  --lyrics ./song.txt \
  --language zh \
  --output-roller ./song.lrc

Start from aligner artifacts

py-roller run \
  --stages a,w \
  --timed-units ./song.timed_units.json \
  --parsed-lyrics ./song.parsed_lyrics.json \
  --output-roller ./song.lrc

For repeated or partially omitted lyrics, choose a repetition mode explicitly:

py-roller run \
  --stages a,w \
  --timed-units ./song.timed_units.json \
  --parsed-lyrics ./song.parsed_lyrics.json \
  --aligner-repetition few \
  --output-roller ./song.lrc

--aligner-repetition accepts:

• none: default standard global_dp_v1 behavior; best when repeated lyric lines are written out in full.
• few: uses global DP as a proposal, then repairs sparse repeated or omitted regions between trusted anchors.
• full: uses per-line candidate generation plus beam search for highly repetitive or anchorless songs.

Rewrite only from an existing alignment result

py-roller run \
  --stages w \
  --alignment-result ./song.alignment.json \
  --writer-backend ass_karaoke \
  --output-roller ./song.ass

Backends and defaults

Backend selection is language-aware. The default language is mul for compatibility, but zh or en gives clearer transcriber/parser defaults when the song language is known.

Transcriber defaults

• zh -> faster_whisper
• en -> faster_whisper
• mul -> faster_whisper

Additional transcriber backends:

• zh also supports --transcriber-backend mms_phonetic for the Chinese phonetic CTC path.
• mul also supports --transcriber-backend wav2vec2_phoneme for the multilingual phoneme CTC fallback.

Parser defaults

• zh -> zh_router_pinyin
• en -> en_arpabet
• mul -> mul_ipa

Other defaults

• aligner backend -> global_dp_v1
• aligner repetition mode -> none
• writer backend -> lrc_ms
• writer spacing -> keep
• cleanup policy -> on-success
• transcriber model store -> ~/.cache/py-roller/models/transcriber
• transcriber device -> auto-detected (CUDA GPU if available, otherwise CPU)
• transcriber VAD filter -> enabled (skips silence to speed up transcription)

Transcriber models and Hugging Face downloads

Transcriber execution is local. py-roller does not send audio to a cloud transcription API.

Model resolution order:

1. Resolve --transcriber-model-name, or use the backend default model name.
2. Look for the model in the py-roller transcriber model store.
3. If not found and offline mode is not enabled, materialize/download the model into the model store.
4. Load the resolved local model path for inference.

Useful model options:

• --transcriber-model-path: local model store root.
• --transcriber-model-name: model alias, Hugging Face repo id, or explicit local path.
• --transcriber-local-files-only: refuse network access and use only local files/cache.

For faster_whisper, aliases such as large-v2, large-v3, and turbo resolve to the corresponding Systran/faster-whisper-* snapshots.

Example with a custom model store:

py-roller run \
  --stages t,p,a,w \
  --audio ./vocals.wav \
  --lyrics ./song.txt \
  --language zh \
  --transcriber-model-path ./models/transcriber \
  --output-roller ./song.lrc

Offline run after the model already exists locally:

py-roller run \
  --stages t,p,a,w \
  --audio ./vocals.wav \
  --lyrics ./song.txt \
  --language zh \
  --transcriber-model-path ./models/transcriber \
  --transcriber-local-files-only \
  --output-roller ./song.lrc

Restricted or unstable networks

Hugging Face model downloads can be affected by proxies, timeouts, and XET/CAS behavior. py-roller exposes the common controls directly:

• --transcriber-hf-xet {auto,on,off}: use off when XET/CAS hangs or fails on your network.
• --transcriber-hf-proxy URL: use one HTTP or SOCKS proxy for model downloads.
• --transcriber-hf-etag-timeout SECONDS: metadata/etag timeout.
• --transcriber-hf-download-timeout SECONDS: large file download timeout.
• --transcriber-hf-max-workers INT: snapshot download parallelism; lower values such as 1 or 2 are often better for fragile proxies.

Avoid XET/CAS when it is unreliable:

py-roller run \
  --stages t,p,a,w \
  --audio ./vocals.wav \
  --lyrics ./song.txt \
  --language zh \
  --transcriber-hf-xet off \
  --output-roller ./song.lrc

Use a local SOCKS proxy and conservative download settings:

py-roller run \
  --stages t,p,a,w \
  --audio ./vocals.wav \
  --lyrics ./song.txt \
  --language zh \
  --transcriber-hf-proxy socks5://127.0.0.1:7890 \
  --transcriber-hf-download-timeout 120 \
  --transcriber-hf-etag-timeout 30 \
  --transcriber-hf-max-workers 2 \
  --output-roller ./song.lrc

audio-core installs SOCKS support through httpx[socks]. If the environment was installed manually and SOCKS support is missing, run:

py-roller install

or install the missing dependency directly:

pip install "httpx[socks]"

VAD filtering

faster-whisper VAD (Voice Activity Detection) filtering skips silent sections during transcription, reducing processing time by 20–40% for songs with pauses or instrumental breaks. It is enabled by default.

Disable VAD filtering if you need word-level timestamps for every segment, or if the VAD model is cutting audio too aggressively:

py-roller run \
  --stages t,p,a,w \
  --audio ./vocals.wav \
  --lyrics ./song.txt \
  --language zh \
  --no-transcriber-vad-filter \
  --output-roller ./song.lrc

GPU auto-detection

When --transcriber-device is not explicitly set, py-roller automatically checks for an available CUDA GPU. If found, the transcriber defaults to device=cuda with compute_type=float16 for significantly faster inference. This can be overridden with --transcriber-device cpu or --transcriber-compute-type int8.

Model pre-download

Pre-download a transcriber model into the local model store so that later pipeline runs can use --transcriber-local-files-only without touching the network:

py-roller cache-model --language zh
py-roller cache-model --language zh --transcriber-model-name large-v3
py-roller cache-model --language zh --transcriber-hf-xet off --transcriber-hf-proxy socks5://127.0.0.1:7890

Writer behavior

LRC

The default writer is lrc_ms, which writes LRC lines with millisecond precision.

Supported writer backends:

• lrc_ms: millisecond precision.
• lrc_cs: centisecond precision.
• lrc_compressed: millisecond precision, with consecutive identical timestamps compressed.
• ass_karaoke: ASS dialogue output with karaoke timing tags.

ASS karaoke

ass_karaoke writes ASS dialogue lines with karaoke timing tags.

Current behavior:

• structural/spacing line output follows --writer-spacing (keep by default).
• display end time prefers matched unit timing instead of blindly extending to the next line.
• unmatched lines receive a short visible-duration fallback.

Example:

py-roller run \
  --stages w \
  --alignment-result ./song.alignment.json \
  --writer-backend ass_karaoke \
  --output-roller ./song.ass

Batch mode

batch uses the same stage semantics as run, but applies them to many tasks.

Directory pairing

Directory mode currently supports:

--pair-by stem

Default candidate globs:

• --audio-glob "*.mp3"
• --lyrics-glob "*.txt"

Matching is non-recursive.

Example:

py-roller batch \
  --stages t,p,a,w \
  --audio ./audio_dir \
  --lyrics ./lyrics_dir \
  --language zh \
  --output-roller ./out_dir

Batch controls

• --jobs N: maximum number of parallel workers.
• --continue-on-error: keep processing remaining tasks after failures.
• --skip-existing: skip tasks whose declared final outputs already exist.
• --manifest jobs.yaml: load explicit per-task paths from YAML instead of pairing by stem.

Parallelism guidance:

• CPU-only: start with --jobs 1 or --jobs 2.
• Single GPU: usually start with --jobs 1.

YAML manifest format

Manifest mode is useful when filenames do not match cleanly by stem.

The manifest defines per-task input and output paths only. It does not override stage selection, language, backend choice, filter settings, jobs, or other batch-level options.

Supported top-level forms:

tasks:
  - id: song01
    audio: ./audio/song01_master.mp3
    lyrics: ./lyrics/song01_final.txt
    output_roller: ./out/song01.lrc

or:

- id: song01
  audio: ./audio/song01_master.mp3
  lyrics: ./lyrics/song01_final.txt
  output_roller: ./out/song01.lrc

Allowed manifest input keys:

• audio
• lyrics
• timed_units
• parsed_lyrics
• alignment_result

Allowed manifest output keys:

• output_vocal_audio
• output_filtered_audio
• output_timed_units
• output_parsed_lyrics
• output_alignment_result
• output_roller

Optional helper key:

• id

Validation rules:

• each task must be a mapping.
• unknown keys are rejected.
• inputs must match the selected chain start.
• outputs must be valid final outputs for the selected chain.
• task ids/stems must be unique.
• final output paths must not conflict across tasks.
• relative paths are resolved relative to the manifest file location.

YAML config for CLI defaults

Use --config to load YAML defaults.

Priority order:

built-in defaults < config YAML < explicit CLI arguments

Section model:

• shared: defaults applied to both run and batch.
• run: currently no extra keys beyond shared.
• batch: defaults for batch-only options such as jobs and skip_existing.

Example:

shared:
  language: zh
  writer_spacing: keep
  writer_backend: lrc_ms
  intermediate: ./tmp/py-roller-artifacts
  cleanup: on-success
  transcriber_device: cpu
  transcriber_model_path: ~/.cache/py-roller/models/transcriber
  transcriber_local_files_only: false
  transcriber_vad_filter: true
  transcriber_hf_xet: auto
  transcriber_hf_proxy: null
  transcriber_hf_download_timeout: 120
  transcriber_hf_etag_timeout: 30
  transcriber_hf_max_workers: 2
  splitter_backend: demucs
  splitter_demucs_model: htdemucs
  splitter_demucs_device: cpu
  splitter_demucs_jobs: 0
  splitter_demucs_overlap: 0.25
  splitter_demucs_segment: 8
  filter_chain:
    - noise_gate
    - dereverb

batch:
  jobs: 2
  audio_glob: "*.mp3"
  lyrics_glob: "*.txt"
  timed_units_glob: "*.json"
  parsed_lyrics_glob: "*.json"
  alignment_result_glob: "*.json"
  continue_on_error: true

filter_chain can be written either as a comma-separated string or as a YAML list. Quote on or off for transcriber_hf_xet if your YAML parser treats them as booleans; py-roller also accepts boolean true/false there as on/off for convenience.

Progress, logs, and cleanup

The project exposes progress in two layers:

• human-readable logs for normal terminal use;
• optional machine-readable JSONL events for GUI frontends such as lrc-roller.

Use --progress-format to choose the progress output mode:

py-roller run ... --progress-format human   # default, terminal-friendly logs
py-roller run ... --progress-format jsonl   # structured PYROLLER_EVENT lines
py-roller run ... --progress-format both    # logs plus structured events

jsonl emits one parseable event per line with the PYROLLER_EVENT prefix, for example:

PYROLLER_EVENT {"type":"download_progress","stage":"model_download","parent_stage":"preflight","repo_id":"Systran/faster-whisper-large-v2","file":"model.bin","bytes_downloaded":1534203904,"bytes_total":3086912962,"progress":0.497}

This is intended for frontends that need reliable stage and download progress instead of parsing mixed logs from tqdm, Demucs, and huggingface_hub. Human-readable mode remains the default so existing CLI workflows are unchanged.

Structured events use progress as the canonical 0.0 to 1.0 field. A percent compatibility alias is still emitted for early GUI integrations. Standard stages are preflight, model_download, splitter, filter, transcriber, parser, aligner, and writer; model download events also include parent_stage: preflight.

Current progress coverage:

• run lifecycle events: run_started, run_completed, and run_failed;
• model preflight and Hugging Face model download events, including cache path, proxy/XET settings, file count, largest file name, bytes downloaded, total bytes when known, and estimated speed;
• heartbeat events during long model downloads and faster-whisper transcription periods;
• splitter/Demucs seconds-based progress as structured splitter events;
• filter phase progress;
• transcriber phase progress, including faster-whisper segment count, last processed audio time, duration hints, and text previews when available;
• parser, aligner, and writer stage events;
• artifact write events and failure events.

In single-task run, human progress is shown as terminal logs/progress bars when supported. In batch, per-task progress is logged to avoid multiple workers fighting for one terminal. GUI frontends should prefer --progress-format jsonl.

Intermediate files live under:

--intermediate/<task-id>/splitter
--intermediate/<task-id>/filter
--intermediate/<task-id>/logs

Default intermediate root:

<system temp>/py-roller-artifacts

Cleanup policy:

• --cleanup on-success: remove per-task intermediate directories after successful tasks.
• --cleanup never: keep intermediate audio and logs for inspection.

Troubleshooting

Check the environment

py-roller doctor

For integrations, use JSON output:

py-roller doctor --output-format json

doctor checks Python, Torch, Torchaudio, faster-whisper, CTranslate2, transformers, SOCKS proxy support, Demucs, and librosa.

If it reports a broken audio/transcriber environment, start with:

py-roller install

Hugging Face model download progress

For restricted networks, prefer disabling HF XET/CAS and using remote-DNS SOCKS:

py-roller run ... \
  --transcriber-hf-xet off \
  --transcriber-hf-proxy socks5h://127.0.0.1:9909

If a model has already been materialized into the py-roller model store, use local-only mode to avoid touching the network on later runs:

py-roller run ... \
  --transcriber-model-path ~/.cache/py-roller/models/transcriber \
  --transcriber-local-files-only

The Hugging Face file-count progress shown by huggingface_hub can appear stuck on large model files. Use --progress-format jsonl or both to get byte-level download_progress events with cache growth, speed, and total size when available.

Interruption and child process cleanup

Batch fail-fast actively stops launching further work after the first task failure when --continue-on-error is not set, and worker cleanup includes a Windows-specific process-tree branch.

For older runs or already orphaned processes, Linux/macOS cleanup examples are still useful:

pkill -TERM -f 'python .*pyroller'
pkill -TERM -f 'demucs.separate|demucs'

If anything still survives:

pkill -KILL -f 'python .*pyroller'
pkill -KILL -f 'demucs.separate|demucs'

Inspect candidates first with:

ps -ef | grep -E 'pyroller|demucs'

Dependency policy

py-roller install prefers the newest validated dependency line for this release:

• Torch/TorchAudio/TorchVision are installed from the official 2.6.0 family for every built-in profile.
• SOCKS proxy support is installed by default through httpx[socks], so Hugging Face downloads do not fail merely because socksio is missing.

If you upgrade or override audio/transcriber packages manually, run py-roller doctor before using transcription-heavy pipelines.