Documentation Index
Fetch the complete documentation index at: https://podonos.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Podonos evaluates speech with native speakers in the target locale. Use the language code below as thelan argument when creating an evaluator (see create_evaluator()).
Supported languages
Here is a list of supported languages and their median turnaround time.| Flag | Code | Language | Turnaround |
|---|---|---|---|
| 🇺🇸 | en-us | English (United States) | 6h 10m |
| 🇬🇧 | en-gb | English (United Kingdom) | 5h 49m |
| 🇦🇺 | en-au | English (Australia) | 4h 34m |
| 🇨🇦 | en-ca | English (Canada) | 12h |
| 🇮🇳 | en-in | English (India) | 4h 15m |
| 🇰🇷 | ko-kr | Korean (Korea) | 5h 7m |
| 🇨🇳 | zh-cn | Mandarin (China) | 7h 31m |
| 🇪🇸 | es-es | Spanish (Spain) | 4h 33m |
| 🇲🇽 | es-mx | Spanish (Mexico) | 5h 46m |
| 🇫🇷 | fr-fr | French (France) | 3h 46m |
| 🇨🇦 | fr-ca | French (Canada) | 12h m |
| 🇩🇪 | de-de | German (Germany) | 5h 40m |
| 🇯🇵 | ja-jp | Japanese (Japan) | 14h 27m |
| 🇮🇹 | it-it | Italian (Italy) | 2h 17m |
| 🇵🇱 | pl-pl | Polish (Poland) | 12h |
| 🇵🇹 | pt-pt | Portuguese (Portugal) | 3h 3m |
| 🇧🇷 | pt-br | Portuguese (Brazil) | 5h 45m |
| 🇱🇰 | si-lk | Sinhala (Sri Lanka) | 48h |
| 🇮🇳 | ta-in | Tamil (India) | 48h |
| 🇮🇳 | kn-in | Kannada (India) | 48h |
| 🇮🇳 | ml-in | Malayalam (India) | 48h |
| 🎧 | audio | General audio file | 12h |
About these turnaround times
The figures above are measured from real evaluations completed within the last 3 months, not theoretical targets. Across all locales, the overall median return time was 5h 56m and the overall average was 23h 28m. The Turnaround column reports the median for each locale. A few things to keep in mind:- Some languages take longer. Sinhala, Tamil, Kannada, and Malayalam have smaller evaluator pools and lower throughput, so individual runs can extend beyond the 48-hour figure shown. We do not throttle them — we wait for qualified native speakers rather than relaxing the bar.
- Larger evaluations take proportionally longer. Figures assume a standard
num_evaland moderate sample count. Very large jobs extend the window. - Time of day and timezone matter. A job launched as the target region wakes up returns faster than one launched at local midnight.