Multilingual Voice AI Training Data for ASR and Diarization
Multilingual voice AI needs real speech from diverse speakers, industries, channels, and languages. InfoBay provides call-center and podcast audio for ASR, diarization, speech analytics, and conversational AI systems.
The problem
Voice models trained on scripted, single-speaker, studio-quality audio break the moment they meet a real caller — background noise, crosstalk, regional accent, and mid-sentence code-switching that scripted corpora never capture. A model tuned only on North American English call-center audio will mis-transcribe an Indian English or Nigerian-accented call at a rate that no amount of post-processing can fully correct, which is why channel and dialect coverage matter as much as raw hour count. InfoBay supplies 3.5M+ hours of real, consented multilingual audio across 45+ languages, including 3.44M+ hours of dual-channel call-center audio, giving ASR, diarization, and conversational voice AI teams the channel fidelity and metadata depth production speech models actually need.
Corpus assets & provenance
Real call-center and podcast audio, not scripted recordings
+InfoBay's audio corpus spans call-center, podcast, and speech-intelligence recordings — 3.44M+ call-center hours and 57K+ podcast hours across 12 podcast languages — tagged by gender, age, industry, channel, dialect, and language, so training sets can be balanced precisely rather than assembled from whatever audio happens to be available.
Dual-channel audio for diarization ground truth
+Where available, call-center audio is captured on separate agent and customer tracks, giving ASR and diarization models clean ground truth for turn-taking, speaker roles, and interruptions without post-hoc source separation — a structural advantage over single-channel or synthetically mixed audio that most public speech corpora rely on.
Depth in languages open datasets don't cover
+The corpus includes deep coverage of languages most commercial datasets treat as an afterthought: Hindi (1.51M hrs), Bengali (377K hrs), Nepali (235.4K hrs), Swahili (111.2K hrs), Luganda (85.5K hrs), and Odia (12.8K hrs), among 45+ languages total — coverage that lets voice models handle South Asian and African speech instead of degrading to a single high-resource language.
Provenance built for regulated voice deployments
+InfoBay's audio is collected via formal enterprise agreements, not web-scraped. Every recording carries language, industry, and channel metadata, with GDPR-eligible lineage documented for review under EU AI Act Article 10, India DPDP Act, CCPA before audio enters a training pipeline.
Speech intelligence beyond ASR
+The same audio corpus that trains ASR and diarization models also supports broader speech-intelligence workloads — sentiment and intent detection from call-center conversations, industry-specific vocabulary coverage, and conversation analytics that depend on knowing not just what was said but which industry, channel, and demographic context it was said in. Because every recording carries industry and channel tags alongside language and dialect, the same dataset can be scoped narrowly for a single ASR fine-tuning run or broadly for a conversational-analytics product without re-collecting audio for each use case.
Specifications
| Language | Hours |
|---|---|
| Hindi | 1.51M hrs |
| Bengali | 377K hrs |
| Nepali | 235.4K hrs |
| Swahili | 111.2K hrs |
| Luganda | 85.5K hrs |
| Arabic (Egypt) | 52.4K hrs |
| Assamese | 58.5K hrs |
| Marathi | 58.3K hrs |
| Odia | 12.8K hrs |
How an engagement works
- 1
Baseline WER and diarization accuracy
Every engagement starts by measuring current word-error rate and diarization accuracy against your target languages and channel types.
- 2
Scope languages, dialects, and channel mix
Audio is scoped to the languages, dialects, industries, and channel configuration (single or dual) your model needs, drawing from the full multilingual catalog.
- 3
Deliver and re-measure WER
Audio ships as WAV/FLAC with JSON transcripts, RTTM diarization files, and a metadata CSV; WER and diarization accuracy are re-measured against the baseline after training.
Answers for buyers
FAQ
How many languages does the audio corpus cover?+
3.5M+ hours across 45+ languages, including deep coverage of South Asian and African languages most datasets don't reach.
Is the audio real speech or synthetic/scripted?+
Real-world, consented recordings collected under enterprise agreements — collected via formal enterprise agreements, not web-scraped — not scraped or scripted.
What is dual-channel audio and why does it matter?+
Agent and customer speech is captured on separate tracks where available, giving diarization models clean ground truth without post-hoc speaker separation.
Do you support code-switched or multilingual conversations?+
Real enterprise call-center audio includes natural code-switching; coverage is not limited to clean single-language boundaries.
What metadata comes with each recording?+
Gender, age, industry, channel, dialect, and language, with dual-channel audio and RTTM diarization labels where available.
What delivery format do you use for audio datasets?+
WAV/FLAC audio with JSON transcripts, RTTM diarization files, and a metadata CSV, compatible with common ASR training pipelines.
Can the audio corpus support use cases beyond ASR, like sentiment or intent detection?+
Yes. Industry and channel tags on the same recordings support conversation analytics, sentiment, and intent detection workloads in addition to ASR and diarization fine-tuning.
Next steps