Datasetpublic

datadriven-company/TTS-German

TTS-German High-quality German speech dataset for TTS and ASR, derived from CML-TTS German. Processing Pipeline Standardize → 24kHz mono WAV, loudness normalize Transcribe → WhisperX word-level timestamps Segment → ≤12s at word boundaries Denoise → DeepFilterNet Quality filter → DNSMOS ≥ 2.5 G2P → IPA phonemes (custom dictionary) Statistics Metric Value Samples 670,509 Hours 1250h Sample rate 24kHz mono Max duration 12s… See the full description on the dataset page: https://huggingface.co/datasets/datadriven-company/TTS-German.

sourceHugging Facecc-by-4.0updated 6mo agoView on Hugging Face

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Dataset Card

TTS-German

High-quality German speech dataset for TTS and ASR, derived from [CML-TTS German](https://huggingface.co/datasets/cmu-lti/cml-tts).

Processing Pipeline

1.Standardize → 24kHz mono WAV, loudness normalize
2.Transcribe → WhisperX word-level timestamps
3.Segment → ≤12s at word boundaries
4.Denoise → DeepFilterNet
5.Quality filter → DNSMOS ≥ 2.5
6.G2P → IPA phonemes (custom dictionary)

Statistics

Metric	Value
Samples	670,509
Hours	1250h
Sample rate	24kHz mono
Max duration	12s

Schema

Column	Type	Description
`__key__`	string	Unique ID
`audio`	Audio (24kHz FLAC)	Lossless audio
`text`	string	Transcript
`ipa`	string	IPA phonemes
`language`	string	Language code
`speaker_id`	string	Speaker identifier
`gender`	string	`male` / `female` / `unknown`
`dnsmos`	float	Quality score (1–5)

Usage

python

from datasets import load_dataset
ds = load_dataset("datadriven-company/TTS-German", split="train")
sample = ds[0]
print(sample["text"])    # transcript
print(sample["ipa"])     # IPA phonemes
# sample["audio"] → {"array": np.ndarray, "sampling_rate": 24000}

License

cc-by-4.0 — derived from CML-TTS German.