--- license: cc-by-nc-sa-4.0 tags: - audio - source-separation --- # CineAudioSynth Synthetic cinematic audio for source separation. 453 scenes, ~22.8 h, 48 kHz / 16-bit / stereo WAV. Each `data/scene_NNNN/` contains: - `linear/` — additive render: `mix.wav` is the BIT-EXACT 16-bit sum of the four stems (`speech`, `music`, `ambience`, `sfx`) — max |mix − Σstems| = 0, verified per scene. Includes `gain_envelope.json` (sidechain ducking envelopes). - `release/` — mastered render of the same scene (compression/limiting/loudness on the mix bus; intentionally non-additive). - `metadata.json` — scene type, DSP/critic settings, SFX event timestamps and descriptions, loudness metrics, additivity measurements. ## Directory structure ``` CineAudioSynth/ ├── README.md ├── ATTRIBUTIONS.md # per-corpus & per-clip credits + citations ├── LICENSE └── data/ ├── scene_0001/ │ ├── linear/ # additive render │ │ ├── speech.wav │ │ ├── music.wav │ │ ├── ambience.wav │ │ ├── sfx.wav │ │ ├── mix.wav # == speech + music + ambience + sfx (bit-exact) │ │ └── gain_envelope.json # sidechain ducking envelopes │ ├── release/ # mastered render (non-additive) │ │ ├── speech.wav music.wav ambience.wav sfx.wav │ │ └── mix.wav │ └── metadata.json # scene plan, DSP settings, loudness, additivity ├── scene_0002/ └── ... # 453 scenes (scene_0001 … scene_0453) ``` Within a scene, every WAV shares the same length, sample rate (48 kHz), bit depth (16-bit) and channel count (stereo). ## Quick start ```python # the whole dataset (~158 GB) ... from huggingface_hub import snapshot_download root = snapshot_download("disco-eth/cineaudiosynth", repo_type="dataset") # ... or grab a single scene from huggingface_hub import hf_hub_download for f in ["linear/speech.wav", "linear/music.wav", "linear/ambience.wav", "linear/sfx.wav", "linear/mix.wav", "release/mix.wav", "metadata.json"]: hf_hub_download("disco-eth/cineaudiosynth", f"data/scene_0001/{f}", repo_type="dataset") ``` ## Load with 🤗 datasets A root `metadata.csv` ships an [audiofolder](https://huggingface.co/docs/datasets/audio_dataset) manifest — one row per WAV with scene-level columns — so the dataset loads directly: ```python from datasets import load_dataset ds = load_dataset("disco-eth/cineaudiosynth", split="train") # decodes audio (needs `torchcodec`) ds = load_dataset("disco-eth/cineaudiosynth", split="train", streaming=True) # stream, no 158 GB download # filter to what you need — e.g. linear-render stems of dialogue-heavy scenes stems = ds.filter(lambda r: r["render"] == "linear" and not r["is_mix"] and r["scene_type"] == "dialogue_heavy") ex = next(iter(stems)) ex["audio"]["array"], ex["audio"]["sampling_rate"], ex["scene_id"], ex["component"] ``` > Decoding uses `torchcodec` (`pip install torchcodec`); to read paths/metadata only, add > `.cast_column("audio", datasets.Audio(decode=False))`. `metadata.csv` columns: `file_name, scene_id, render, component, is_mix, scene_type, has_music, has_ambience, n_sfx_events, music_tag, ambience_cue, linear_additivity_verified`. ## Loading a scene ```python import soundfile as sf scene = "data/scene_0001" # linear (additive) render — mix is the bit-exact 16-bit sum of the four stems speech, sr = sf.read(f"{scene}/linear/speech.wav") music, _ = sf.read(f"{scene}/linear/music.wav") ambience, _ = sf.read(f"{scene}/linear/ambience.wav") sfx, _ = sf.read(f"{scene}/linear/sfx.wav") linear_mix, _ = sf.read(f"{scene}/linear/mix.wav") # linear_mix == speech + music + ambience + sfx (max abs diff == 0) # release render — same scene, mastered on the mix bus (intentionally non-additive) release_mix, _ = sf.read(f"{scene}/release/mix.wav") ``` ## Verify additivity ```python import numpy as np, soundfile as sf lin = "data/scene_0001/linear" stems = sum(sf.read(f"{lin}/{s}.wav")[0] for s in ["speech", "music", "ambience", "sfx"]) mix, _ = sf.read(f"{lin}/mix.wav") print("max |mix - Σstems|:", np.max(np.abs(mix - stems))) # -> 0.0 ``` ## Iterate every scene ```python import glob, os, soundfile as sf root = "data" # or the path returned by snapshot_download(...) for scene in sorted(glob.glob(os.path.join(root, "scene_*"))): mix, sr = sf.read(os.path.join(scene, "linear", "mix.wav")) targets = {s: sf.read(os.path.join(scene, "linear", f"{s}.wav"))[0] for s in ["speech", "music", "ambience", "sfx"]} # ... train / evaluate your separator on (mix, targets) ``` ## Reading scene metadata ```python import json meta = json.load(open("data/scene_0001/metadata.json")) meta["scene_type"] # e.g. "balanced" meta["linear_additivity_verified"] # True meta["scene_plan"]["events"] # [{"timestamp": 3.0, "description": "..."}, ...] meta["outputs"]["linear"]["loudness"] # integrated LUFS / true-peak dBTP ``` `linear/gain_envelope.json` holds the per-stem, speech-triggered sidechain-ducking keyframes used to build the linear mix. ## License & attribution This dataset is released under **CC BY-NC-SA 4.0** (non-commercial, share-alike). Individual stems remain governed by the licenses of their source material; music and sound-effect assets appear only within mixed scenes and may not be extracted and re-published as standalone collections. Full per-track and per-clip credits, plus citations for all source corpora, are in [`ATTRIBUTIONS.md`](./ATTRIBUTIONS.md). Source corpora and asset libraries used by the CineAudioGen pipeline: | Source | Content | License | |---|---|---| | [Expresso](https://speechbot.github.io/expresso/) | speech | CC BY-NC 4.0 | | [RAVDESS](https://zenodo.org/records/1188976) | speech | CC BY-NC-SA 4.0 | | [ASED](https://github.com/Ethio2021/ASED_V1) | speech | per its distribution page (cite authors) | | [NonverbalTTS](https://huggingface.co/datasets/deepvk/NonverbalTTS) | speech | Apache-2.0 (annotations); audio CC BY / CC BY-NC | | [FSD50K](https://zenodo.org/records/4060432) | SFX & ambience | CC BY 4.0; per-clip CC0 / CC BY / CC BY-NC / CC Sampling+ | | [FMA](https://github.com/mdeff/fma) | music | per-track CC licenses | | [Chosic](https://www.chosic.com/) | music | per-track (CC and artist terms; attribution required) | | [no-copyright-music.com](https://www.no-copyright-music.com/) | music | royalty-free (provider terms; attribution required) | When using this dataset, cite the source corpora as listed in `ATTRIBUTIONS.md`. Research use only. Not for commercial use. Derivatives must be shared under the same license (CC BY-NC-SA 4.0).