> For the complete documentation index, see [llms.txt](https://documentation.proto.cx/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.proto.cx/docs/developers/local-languages/language-training/voice-data-collection.md).

# Voice data collection

### Do you already have voice data?

#### Option A – You already have recordings

If you have existing audio in the target language, provide:

* Audio files (minimum 50 hours preferred for ASR and TTS)
* Matching transcripts – every audio file must be paired with an exact text match

#### Option B – You need to create recordings

Follow steps 1 through 6 below.

***

### Step 1 – Prepare your sentence list

Create a list of sentences in the target language for recording.

The sentence list should:

* Contain 10,000 to 15,000 sentences
* Cover a wide variety of vocabulary, including domain-specific terms, product names, and proper nouns relevant to your use case
* Be written in the target language only

If your source material is in English, translate the sentences into the target language before preparing the list.

**Output format :**

| ID  | English                                   | Target Language                |
| --- | ----------------------------------------- | ------------------------------ |
| 001 | How do I register?                        | \[Sentence in target language] |
| 002 | You can register by visiting our website. | \[Sentence in target language] |
| 003 | The office is open from Monday to Friday. | \[Sentence in target language] |

***

### Step 2 – Set up your recording equipment

Audio quality is critical. Poor recordings significantly reduce AI voice performance.

| Requirement         | Specification                                         |
| ------------------- | ----------------------------------------------------- |
| Minimum hardware    | Entry-level laptop with USB port                      |
| Audio               | Noise-cancelling headset with high-quality microphone |
| Recommended headset | Sennheiser EPOS or equivalent                         |

> A built-in laptop microphone is not acceptable.

**Before recording, test your setup.** Record a short clip – about 30 seconds – and check that it meets the following quality standards:

* Audio is clear and free from background noise
* No unnatural pauses or long silences at the start or mid-sentence
* No echo or reverb (a hollow, bouncing sound)
* Volume is consistent throughout – not too soft, not too loud, and no distortion
* No handling noise from the microphone or headset cable
* Speech is clear and at a natural pace
* No interruptions such as typing sounds, notifications, or sudden noises

Do not begin a full recording session until you are satisfied with your test clip.

**Suggested tools for checking audio quality:**

* [**snr.audio**](http://snr.audio) – upload a short clip to get a reading on how clean your audio is relative to background noise
* [**mic-tests.github.io/background-noise-analyzer**](http://mic-tests.github.io/background-noise-analyzer) – test your microphone in real time before starting a session
* [**Audacity**](https://www.audacityteam.org/) (free desktop app) – record and visually inspect your audio for noise, inconsistencies, and levels

**Recording environment:**

* Use a quiet room with soft surfaces – carpets, curtains, and cushions absorb echo
* Turn off fans, air conditioning, and other constant noise sources
* Close windows and doors
* Keep the microphone at the same distance from your mouth throughout every session
* Do not record in large, empty rooms – they produce echo
* Use a pop filter if available to reduce harsh sounds on letters like "p" and "b"
* Use the same room, same device, and same setup across all sessions

***

### Step 3 – Record for text-to-speech (TTS)

TTS recordings give your AI Agent its voice. The goal is consistency.

**All TTS recordings must come from a single speaker.**

* Same speaker throughout all sessions – do not switch between speakers
* Consistent tone and pitch across every recording
* Clear pronunciation – every word articulated distinctly
* Natural pace – not too fast, not artificially slow
* Natural pauses at punctuation – commas, full stops, and question marks should each have a brief pause
* Wide vocabulary – the sentence list should cover every word and term the AI Agent may need to say

***

### Step 4 – Record for speech-to-text (ASR)

ASR recordings train your AI Agent to understand real users speaking naturally. The goal is diversity.

* Multiple speakers – include male and female voices, different ages
* Different accents and dialects – reflect the actual user base where possible
* Varied intonations – record questions, statements, and words with different emphasis
* Natural speaking styles – speakers should use their natural pace and volume

For ASR, a single sentence can have more than one recording. Multiple speakers recording the same sentence improves the range of users your AI Agent can understand.

> TTS recordings can be reprocessed for ASR training. ASR recordings cannot be used for TTS.

***

### Step 5 – Name, save, and organise your files

Each recording must be saved as a separate audio file. File names must match the sentence ID exactly, and files must be organised into separate TTS and ASR folders for delivery.

**Format:** .mp3 or .wav

**TTS – single language:**

```
001.mp3
002.mp3
003.mp3
```

**TTS – multiple languages (add the language code):**

```jsx
001_en.mp3
001_fr.mp3
```

**ASR – multiple speakers (add the speaker number):**

```
001_speaker1.mp3
001_speaker2.mp3
002_speaker1.mp3
```

**ASR – multiple languages and speakers:**

```
001_fr_speaker1.mp3
001_fr_speaker2.mp3
001_rw_speaker1.mp3
```

**Folder structure for delivery:**

```
[Language name]/
├── TTS/
│   ├── 001_fr.mp3
│   ├── 002_fr.mp3
│   └── sentence_list.xlsx
└── ASR/
    ├── 001_fr_speaker1.mp3
    ├── 001_fr_speaker2.mp3
    ├── 002_fr_speaker1.mp3
    └── sentence_list.xlsx
```

Include the sentence list spreadsheet in each folder, with every sentence ID mapped to its corresponding text.

***

### Step 6 – Final checklist before submission

**Sentence list**

✅ 10,000 to 15,000 sentences in the target language\
✅ Wide vocabulary coverage including domain-specific terms and proper nouns\
✅ Every sentence has a unique ID number

**TTS recordings**

✅ All recordings made by a single, consistent speaker\
✅ Tone and pitch consistent across all sessions\
✅ Every sentence ID has exactly one matching audio file\
✅ Audio is clear – no background noise, no echo, no distortion\
✅ Files named correctly and saved in the correct format\
✅ Sentence list spreadsheet included in the TTS folder

**ASR recordings**

✅ Multiple speakers recorded\
✅ Varied accents, ages, and intonations represented\
✅ Every audio file is mapped to its sentence ID\
✅ Audio is clear\
✅ Files named correctly\
✅ Sentence list spreadsheet included in the ASR folder

***

### Common mistakes to avoid

| Mistake                                                 | Why it matters                                        |
| ------------------------------------------------------- | ----------------------------------------------------- |
| Using a built-in laptop microphone                      | Audio quality is too low for training                 |
| Background noise in recordings                          | Degrades model performance significantly              |
| Long pauses at the start of a recording or mid-sentence | Audio will not align with the transcript correctly    |
| Switching speakers mid-TTS session                      | Produces an inconsistent AI Agent voice               |
| Using only one speaker for ASR                          | Limits the range of users the AI Agent can understand |
| Incorrect or inconsistent file naming                   | Files cannot be matched to their transcripts          |
| Missing files                                           | Incomplete datasets delay or prevent training         |