Audio Dataset Collection
Training AI Ears

Speech recognition converts spoken words to text (like Siri/Alexa). Voice cloning recreates a specific person's voice for text-to-speech. Sound classification identifies audio types (music genre, environmental sounds, instruments). Each requires different data: speech needs audio+text pairs, cloning needs high-quality voice samples, classification needs labeled audio categories.

Minimum viable: 1-5 hours of transcribed audio. Good quality: 10-50 hours. Production models: 100-1000+ hours. Key is diversity - different speakers, accents, background conditions. For specific tasks (like wake word detection), you can get away with less: 500-1000 examples of the target phrase from diverse speakers.

Always use WAV or FLAC for training data. WAV is uncompressed with perfect quality but large files. FLAC is lossless compression with same quality as WAV but smaller files. Avoid MP3 - compression removes audio information that AI needs, hurting accuracy. You can convert to MP3 later for deployment if needed, but never train on compressed audio.

Not necessarily! Environment matters more than equipment. A $50 USB microphone in a quiet, treated room beats a $1000 studio mic in a noisy environment. Key factors: quiet room, consistent distance from mic, pop filter, and proper levels. Many successful datasets used basic USB mics. Start with what you have, upgrade only if quality testing shows issues.

Prevention is best: record in quiet spaces, close windows, turn off AC/fans. For existing noise: use Audacity's noise reduction effect, normalize volume levels, and apply high-pass filter to remove low-frequency rumble. For consistent noise (like computer fan), record a noise sample and use it for noise reduction. Quality check: listen with headphones to every recording.

For speech recognition and voice cloning: always use mono (single channel). Stereo doesn't provide benefits for voice tasks and doubles file size. For music classification: use stereo to preserve spatial information. For environmental sounds: mono is usually fine unless spatial positioning is important for your use case. Most AI models expect mono input for speech tasks.

For speech recognition: 16kHz (16,000 Hz) is sufficient and matches most speech models. For high-quality voice cloning: 22kHz or 44.1kHz for better voice characteristics. For music classification: 44.1kHz (CD quality) to capture full frequency range. For environmental sounds: 16-22kHz usually adequate. Higher sample rates mean larger files but not always better performance.

Extremely accurate - 99%+ accuracy ideal. Every transcription error teaches AI the wrong word-sound mapping. Use automatic transcription (Whisper) as starting point, then manually review and correct every mistake. Pay special attention to: proper names, technical terms, numbers, punctuation, and filler words (um, uh) if you want AI to recognize natural speech patterns.

NO - that's copyright infringement. Use royalty-free music (Free Music Archive, YouTube Audio Library), public domain works, or properly licensed commercial music. For learning, use existing academic datasets: GTZAN (music genres), LibriSpeech (audiobooks), ESC-50 (environmental sounds). For commercial projects, ensure you have rights to all training data or use original recordings.

Include variety in: age (teens to seniors), gender, accents/regional dialects, native languages, speaking styles, and recording environments. Recruit friends, family, colleagues, or use crowdsourcing platforms. Aim for 50-100+ different speakers for robust models. Document speaker demographics for analysis. Balance your dataset - avoid 90% male speakers if you want AI to work for everyone.

Inconsistent volume levels across recordings, background noise, using compressed formats (MP3), inaccurate transcriptions, lack of speaker diversity, wrong sample rates, inconsistent recording distances, not normalizing audio, including silence/noise segments, and poor file organization. These mistakes directly impact model performance and are hard to fix after training begins.

Standard structure: separate folders for audio files and labels. For classification: audio files organized by class folders. For speech recognition: audio files paired with transcription files (JSON, CSV, or TXT). Include metadata files with speaker info, recording conditions, and audio specifications. Maintain consistent naming conventions. Split data: 70% training, 15% validation, 15% test. Document everything for reproducibility.