> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fluidinference.com/llms.txt
> Use this file to discover all available pages before exploring further.

# ASR Getting Started

> Batch and streaming transcription with Parakeet models.

## When to Use

* **Transcribing recordings or files** — Use batch ASR (this page). 210x real-time, 2.5% WER on English.
* **Live captions while user speaks** — Use [Streaming ASR](/asr/streaming) with Parakeet EOU.
* **Domain-specific terms keep getting wrong** — Add [Custom Vocabulary](/asr/custom-vocabulary) boosting (91.7% F-score on earnings calls).

## Models

| Model               | Languages    | Audio Length | Use Case               |
| ------------------- | ------------ | ------------ | ---------------------- |
| **Parakeet TDT v3** | 25 European  | \~15s chunks | Default — multilingual |
| **Parakeet TDT v2** | English only | \~15s chunks | Best English accuracy  |
| **Parakeet EOU**    | English      | 320ms chunks | Real-time streaming    |

## Batch Transcription

Real-time factor: \~120x on M4 Pro (1 minute of audio in \~0.5 seconds).

```swift theme={null}
import FluidAudio

Task {
    let models = try await AsrModels.downloadAndLoad(version: .v3) // .v2 for English-only
    let asrManager = AsrManager(config: .default)
    try await asrManager.initialize(models: models)

    let samples = try AudioConverter().resampleAudioFile(
        path: "path/to/audio.wav"
    )
    let result = try await asrManager.transcribe(samples, source: .system)
    print("Transcription: \(result.text)")
    print("Confidence: \(result.confidence)")
}
```

### Transcribing from a File URL

```swift theme={null}
let audioURL = URL(fileURLWithPath: "/path/to/audio.wav")
let result = try await asrManager.transcribe(audioURL, source: .system)
print(result.text)
```

<Warning>
  Do not parse WAV/PCM bytes by hand. Always convert with `AudioConverter` so differing bit depths, channel layouts, metadata chunks, or compressed formats get normalized to the 16 kHz mono Float32 tensors that Parakeet expects.
</Warning>

## Choosing a Model Version

* **v2** — English only. Tighter vocabulary, better recall on long-form English audio.
* **v3** — 25 European languages. English accuracy is still strong, but the broader vocab slightly trails v2 on rare words.

Both share the same API surface—set `AsrModelVersion` in code or pass `--model-version` in the CLI.

```swift theme={null}
let models = try await AsrModels.downloadAndLoad(version: .v2)
```

## Benchmarks

[LibriSpeech test-clean](https://huggingface.co/datasets/openslr/librispeech_asr) (2,620 files, 5.4h audio):

| Model                | WER  | RTFx |
| -------------------- | ---- | ---- |
| Parakeet TDT v3      | 2.5% | 156x |
| Parakeet TDT v2      | 2.1% | 146x |
| Parakeet EOU (320ms) | 4.9% | 12x  |

[FLEURS](https://huggingface.co/datasets/google/fleurs) (14,085 files, 44.9h audio, 25 languages):

| Model           | Avg WER | RTFx |
| --------------- | ------- | ---- |
| Parakeet TDT v3 | 14.7%   | 210x |

See [full benchmarks](/reference/benchmarks) for per-language breakdown.

## CLI

```bash theme={null}
# Transcribe (multilingual)
swift run fluidaudio transcribe audio.wav

# English-only (better recall)
swift run fluidaudio transcribe audio.wav --model-version v2

# Multiple files in parallel
swift run fluidaudio multi-stream audio1.wav audio2.wav

# Benchmark on LibriSpeech
swift run fluidaudio asr-benchmark --subset test-clean --max-files 50
```