How much training data do you need for asr model training?

What the model actually needs

Building or fine-tuning speech recognition for Indian languages from scratch or from a multilingual base model.

• Hundreds to thousands of hours of verbatim-transcribed speech
• Wide speaker diversity: age, gender, region, education, recording condition
• Speaker-disjoint train/dev/test splits

Metrics that tell you when you have enough

Collect against a metric, not against a number of hours. When a pilot batch moves the metric and a second batch of the same profile moves it less, you are at the point where composition, not volume, is the constraint.

• Word error rate overall and per dialect
• Entity error rate on names and numbers
• Code-switch token accuracy

Common mistakes

• Read-speech-only corpora that do not transfer to spontaneous audio
• Speaker leakage across splits inflating reported accuracy
• Normalised-only transcripts with the raw text discarded

A sensible collection sequence

Services that supply this data

This use case is normally served by asr training data, speech data collection, transcription services. Most programmes combine two of them, because raw collection without matched annotation rarely moves an applied metric on its own.

Hold back an honest evaluation set

Reserve deployment-condition data that never enters training. Teams that evaluate on data recorded in the same sessions as their training data consistently overestimate real-world performance, then discover the gap after launch.