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4 values
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4 values
source
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14 values
yt_train_segment_0_1.wav
{ "bytes": [ 82, 73, 70, 70, 164, 163, 1, 0, 87, 65, 86, 69, 102, 109, 116, 32, 16, 0, 0, 0, 1, 0, 1, 0, 128, 62, 0, 0, 0, 125, 0, 0, 2, 0, 16, 0, 100, 97, 116, ...
To go out into that field and exchange your laundry.
en
clean
youtube_harvest
noisy_yt_train_segment_0_1.wav
{"bytes":"UklGRqSjAQBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYCjAQBbCnoKqwpSCw0LgAr3CdoJTQhoBzgHRA(...TRUNCATED)
To go out into that field and exchange your laundry.
en
synthesized_desktop
youtube_harvest_noisy
yt_train_segment_0_10.wav
{"bytes":"UklGRqTDAQBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYDDAQA6/2n/jf+J/4D/a/9A/zb/Uf+I/9n/7f(...TRUNCATED)
Independence for India was just round the corner, as Rachel mentioned.
en
clean
youtube_harvest
noisy_yt_train_segment_0_10.wav
{"bytes":"UklGRqTDAQBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYDDAQBRAEIAKgAbABQABgDu/9D/rv+X/4v/fP(...TRUNCATED)
Independence for India was just round the corner, as Rachel mentioned.
en
synthesized_desktop
youtube_harvest_noisy
yt_train_segment_0_100.wav
{"bytes":"UklGRqT7AQBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYD7AQD8//7/AAAAAAEAAAAEAAUABwALAAsACA(...TRUNCATED)
Depicts more trust, or certain brands depict more trust because.
en
clean
youtube_harvest
yt_train_segment_0_101.wav
{"bytes":"UklGRqQLAwBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYALAwAYABMAFwAOABMAEgAPABIAFwAbABgAHA(...TRUNCATED)
"Like this, if you do this, somebody will trust you. If you do this, somebody will trust you. Trust (...TRUNCATED)
en
clean
youtube_harvest
noisy_yt_train_segment_0_101.wav
{"bytes":"UklGRqQLAwBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYALAwA7ADcAMgAvAC4ALwAyADQAOQBBAEAANw(...TRUNCATED)
"Like this, if you do this, somebody will trust you. If you do this, somebody will trust you. Trust (...TRUNCATED)
en
synthesized_desktop
youtube_harvest_noisy
yt_train_segment_0_103.wav
{"bytes":"UklGRqQvAgBXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYAvAgDY/9r/3f/Z/9b/1//Z/9n/2//Z/9z/3f(...TRUNCATED)
They have done right by the consumer, by the various stakeholders that.
en
clean
youtube_harvest
yt_train_segment_0_104.wav
{"bytes":"UklGRqRbBABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYBbBAACAAEAAAABAAUABwAHAA0ADQATABQAFw(...TRUNCATED)
"And therefore, today that brand commands significant trust. So I think trust is again one of those (...TRUNCATED)
en
clean
youtube_harvest
yt_train_segment_0_106.wav
{"bytes":"UklGRqQvBABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YYAvBADp/+v/8/8AAPv/9P/w//L/7//y//3/CA(...TRUNCATED)
"As a business, you're faced with so many different decisions at some point, right? Where you can cu(...TRUNCATED)
en
clean
youtube_harvest
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Noisy Hinglish ASR Corpus

A high-fidelity, mixed Hindi, English, Hinglish, and noise-robust ASR corpus designed for low-latency, localized voice assistant applications. Features conversational speech, heavy code-switching (English words embedded in Hindi structure), synthetically augmented desktop noise, and explicit non-speech negative frames for VAD optimization.

Dataset Summary

  • Total Samples: 28,681 audio recordings (main splits)
  • Total Duration: 36.6 hours
  • Format: Signed 16-bit PCM WAV, mono, 16,000 Hz
  • Languages: Hindi (hi), English (en), Hinglish code-switched (hinglish), Neutral non-speech (neutral)
  • Noise profiles: clean, synthesized_desktop, room_background, pure_silence
  • Validation: 100% passed via strict structural validation

Splits

Split Samples Duration Languages
train 23,512 30.26h hi (7,726), en (6,758), hinglish (6,028), neutral (3,000)
val 2,331 2.91h hi (819), en (526), hinglish (536), neutral (450)
test 2,838 3.47h hi (832), en (504), hinglish (1,052), neutral (450)

Benchmark Sets

Benchmark Samples Duration Description
clean_hi 500 37.9m Clean high-fidelity Hindi
hinglish 1,036 62.9m Clean code-switched Hinglish
noisy_hi 250 25.9m Noisy/acoustic stressed Hindi
negatives 200 12.5m Ambient noise / silence (expect empty transcription)

Data Structure

data/
β”œβ”€β”€ train/           # 23 parquet shards
β”œβ”€β”€ val/             # 3 parquet shards
β”œβ”€β”€ test/            # 3 parquet shards
└── benchmark/
    β”œβ”€β”€ clean_hi/    # 1 parquet shard
    β”œβ”€β”€ hinglish/    # 1 parquet shard
    β”œβ”€β”€ noisy_hi/    # 1 parquet shard
    └── negatives/   # 1 parquet shard

Parquet Schema

Column Type Description
file_name string Original WAV filename
audio struct(bytes: binary, path: string) Embedded WAV bytes + relative path
transcription string Ground truth transcript (empty for negatives)
language string hi, en, hinglish, or neutral
noise_type string clean, synthesized_desktop, room_background, pure_silence
source string Origin dataset/source

Loading

from datasets import load_dataset

dataset = load_dataset("addyo07/noisy-hinglish-asr", streaming=True)
train = dataset["train"]
print(next(iter(train)))

Scripts

Corpus compilation and utilities are in scripts/:

  • compile_corpus.py β€” Assemble raw audio sources into structured corpus
  • pack_to_parquet.py β€” Serialize WAV corpus to chunked Parquet
  • unpack_from_parquet.py β€” Restore Parquet back to WAV + metadata
  • validate_corpus.py β€” Structural validation of audio files

Related Models

This dataset was used to fine-tune addyo07/qwen-asr-0.6b-noisy-hinglish.

License

Apache 2.0

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Models trained or fine-tuned on addyo07/noisy-hinglish-asr