Transcription

Convert audio to text with high accuracy using automatic speech recognition (ASR).

Overview

Izwi’s transcription feature converts spoken audio into written text. Capabilities include:

High accuracy — State-of-the-art speech recognition
Multiple formats — Support for WAV, MP3, M4A, FLAC, and more
Language detection — Automatic language identification
Timestamps — Optional word-level timing through a forced aligner
Summaries — Optional AI summaries for persisted speech-text jobs
Local processing — Complete privacy, no cloud

Getting Started

Download an ASR Model

izwi pull Parakeet-TDT-0.6B-v3

Transcribe Audio

izwi transcribe audio.wav

Using the CLI

Basic Usage

izwi transcribe <audio-file>

Options

Option	Description	Default
`--model`, `-m`	ASR model to use	`parakeet-tdt-0.6b-v3`
`--language`, `-l`	Language hint	auto-detect
`--format`, `-f`	Output format	`text`
`--output`, `-o`	Output file	stdout
`--word-timestamps`	Include word timing	—

Examples

Basic transcription:

izwi transcribe meeting.wav

Save to file:

izwi transcribe meeting.wav --output transcript.txt

JSON output with metadata:

izwi transcribe meeting.wav --format json --output transcript.json

With word timestamps:

izwi transcribe meeting.wav --format verbose_json --word-timestamps

Specify language:

izwi transcribe audio.wav --language en
izwi transcribe audio.wav --language es

Using the Web UI

Navigate to Transcription in the sidebar
Choose a mode
Upload an audio file or record directly
Select the required model
Click Transcribe or submit the selected workflow
View, copy, summarize, or download the result

The Transcription workspace has three modes:

Mode	Best for	Notes
Transcription	Single-speaker or general ASR	Streaming is enabled by default. Word timestamps require a ready `Qwen3-ForcedAligner-0.6B` aligner and disable streaming.
Speaker Attributed ASR	Granite Speech speaker-turn transcripts	Requires `Granite-Speech-4.1-2B-Plus`, accepts speaker expectation hints, and disables streaming/timestamps.
Diarization	Speaker-separated timelines with start/end times	Uses the diarization pipeline and optional ASR/aligner models.

Features

Drag and drop — Upload files easily
Record — Transcribe directly from microphone
Mode switch — Choose Transcription, Speaker Attributed ASR, or Diarization
Summaries — Generate or regenerate AI summaries for saved records
Copy — One-click copy to clipboard
Download — Save as text or JSON

Using the API

Endpoint

POST /v1/audio/transcriptions

Request (multipart/form-data)

Field	Type	Description
`file`	File	Audio file to transcribe
`model`	String	Model name
`language`	String	Language code (optional)
`response_format`	String	`text`, `json`, `verbose_json`, `srt`, or `vtt`
`stream`	Boolean/String	Enable SSE transcript events (`true`, `1`, `yes`, or `on`)

Example (curl)

curl -X POST http://localhost:8080/v1/audio/transcriptions \
  -F "file=@audio.wav" \
  -F "model=Parakeet-TDT-0.6B-v3" \
  -F "response_format=json"

Response (JSON)

{
  "text": "Hello, this is a transcription test."
}

Response (verbose_json)

{
  "text": "Hello, this is a transcription test.",
  "language": "en",
  "duration": 3.5,
  "processing_time_ms": 812.4,
  "rtf": 0.23,
  "izwi_asr_diagnostics": null
}

Streaming responses emit SSE payloads with type values such as transcript.text.delta, transcript.text.done, and error. See the API Reference for JSON input, streaming events, upload limits, and exact response shapes.

Persisted Speech-Text Jobs

The web UI uses /v1/speech-to-text/jobs for saved transcription records, Speaker Attributed ASR records, and diarization jobs:

curl -X POST "http://localhost:8080/v1/speech-to-text/jobs?job_kind=transcription" \
  -F "file=@meeting.wav" \
  -F "model_id=Parakeet-TDT-0.6B-v3" \
  -F "generate_summary=true"

Use job_kind=speaker_attributed_asr or job_kind=saa for Granite speaker-turn transcripts, and job_kind=diarization for speaker timelines.

Supported Audio Formats

Format	Extension	Notes
WAV	`.wav`	Best quality, recommended
MP3	`.mp3`	Widely compatible
M4A	`.m4a`	Apple format
FLAC	`.flac`	Lossless
OGG	`.ogg`	Open format
WebM	`.webm`	Web recordings

Available Models

Model	Size	Accuracy	Speed
`Parakeet-TDT-0.6B-v3`	9.4 GB	Strong baseline	Medium
`Whisper-Large-v3-Turbo`	1.5 GB	Strong multilingual baseline	Medium
`Qwen3-ASR-0.6B-GGUF`	1.0 GB	Good	Fast
`Qwen3-ASR-1.7B-GGUF`	2.5 GB	Better	Medium
`VibeVoice-ASR`	16.2 GB	Long-form Microsoft ASR checkpoint	Medium
`Nemotron-3.5-ASR-Streaming-0.6B`	2.37 GB	40-locale NVIDIA FastConformer-RNNT; native offline transcription with prompt-conditioned language control	Medium
`Granite-Speech-4.1-2B-Plus`	4.2 GB	IBM rich transcription model with prompt guidance, speaker-attributed output, and word timestamps	Medium
`LFM2.5-Audio-1.5B-GGUF`	1.2 GB	Good integrated speech model	Medium
`Voxtral-Mini-4B-Realtime-2602`	8 GB	Rust/Candle offline transcription; realtime planned	Medium

Use larger models for:

Noisy audio
Accented speech
Technical vocabulary

Output Formats

Text

Plain text transcript:

Hello, this is a transcription test.

JSON

{
  "text": "Hello, this is a transcription test."
}

Verbose JSON

Includes language, duration, processing-time, realtime-factor, and optional runtime diagnostics. Word-level timestamps are not currently returned by this endpoint.

{
  "text": "Hello, this is a transcription test.",
  "language": "en",
  "duration": 3.5,
  "processing_time_ms": 812.4,
  "rtf": 0.23,
  "izwi_asr_diagnostics": null
}

Tips for Best Results

Use quality audio — Clear recordings transcribe better
Minimize noise — Background noise reduces accuracy
Proper format — WAV files work best
Right model size — Larger models for difficult audio
Language hints — Specify language if known

Home

Reference & Help

Transcription

Overview

Getting Started

Download an ASR Model

Transcribe Audio

Using the CLI

Basic Usage

Options

Examples

Using the Web UI

Features

Using the API

Endpoint

Request (multipart/form-data)

Example (curl)

Response (JSON)

Response (verbose_json)

Persisted Speech-Text Jobs

Supported Audio Formats

Available Models

Output Formats

Text

JSON

Verbose JSON

Tips for Best Results

See Also

​Overview

​Getting Started

​Download an ASR Model

​Transcribe Audio

​Using the CLI

​Basic Usage

​Options

​Examples

​Using the Web UI

​Features

​Using the API

​Endpoint

​Request (multipart/form-data)

​Example (curl)

​Response (JSON)

​Response (verbose_json)

​Persisted Speech-Text Jobs

​Supported Audio Formats

​Available Models

​Output Formats

​Text

​JSON

​Verbose JSON

​Tips for Best Results

​See Also

Overview

Getting Started

Download an ASR Model

Transcribe Audio

Using the CLI

Basic Usage

Options

Examples

Using the Web UI

Features

Using the API

Endpoint

Request (multipart/form-data)

Example (curl)

Response (JSON)

Response (verbose_json)

Persisted Speech-Text Jobs

Supported Audio Formats

Available Models

Output Formats

Text

JSON

Verbose JSON

Tips for Best Results

See Also