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Datasets:
ultrasafe-ai
/
asr-eval-noise-clean-mix-v1-1k

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
soundfolder
Languages:
English
Size:
1K - 10K
Tags:
audio
speech
asr
benchmark
restaurant
Libraries:
Datasets
License:
Dataset card Data Studio Files
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  1. README.md +73 -0
  2. test/0026_Restaurant_snr0dB.wav +3 -0
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README.md ADDED
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+ ---
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+ license: mit
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+ task_categories:
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+ - automatic-speech-recognition
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+ language:
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+ - en
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+ tags:
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+ - audio
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+ - speech
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+ - asr
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+ - benchmark
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+ - noisy-speech
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+ size_categories:
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+ - 1K<n<10K
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: train/metadata.csv
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+ ---
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+
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+ # ASR Eval — Noisy Speech Mix v1 (1K)
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+
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+ A 1,000-sample English speech benchmark dataset for evaluating ASR systems under realistic noisy conditions.
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+
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+ ## Dataset Summary
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+
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+ Each sample contains a real human speech recording mixed with ambient background noise at a controlled Signal-to-Noise Ratio (SNR = 0 dB), making it a challenging benchmark for ASR robustness.
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+
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+ ## Dataset Structure
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+
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+ | Column | Type | Description |
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+ |---|---|---|
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+ | `uuid` | string | Unique sample identifier |
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+ | `audio` | Audio (16 kHz) | Noisy speech — voice mixed with background noise |
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+ | `duration` | float | Audio duration in seconds |
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+ | `transcript` | string | Ground truth transcript |
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("ultrasafe-ai/asr-eval-noise-clean-mix-v1-1k", split="test")
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+
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+ sample = ds[0]
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+ print(sample["transcript"])
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+ # → "this election is very important to..."
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+
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+ audio_array = sample["audio"]["array"] # numpy array, float32
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+ sample_rate = sample["audio"]["sampling_rate"] # 16000
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+ duration_sec = sample["duration"] # e.g. 9.32
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+ ```
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+
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+ ## Benchmarking Example
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+
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+ ```python
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+ # Transcribe and compute WER
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+ from jiwer import wer
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+
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+ reference = sample["transcript"]
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+ hypothesis = your_asr_model(sample["audio"]["array"])
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+ score = wer(reference, hypothesis)
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+ print(f"WER: {score:.3f}")
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+ ```
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+
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+ ## Dataset Stats
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+
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+ - **Samples:** 1,000
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+ - **Language:** English
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+ - **Sample rate:** 16,000 Hz (mono)
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+ - **Avg duration:** ~8–12 seconds per sample
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+ - **Noise condition:** Ambient background noise mixed at SNR = 0 dB (equal loudness — challenging)
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