Skip to main content

GENSHI Works STT SDK — high-accuracy domain-specific speech-to-text

Project description

GENSHI Works STT SDK

High-accuracy domain-specific speech-to-text SDK. Supports batch transcription and realtime streaming with built-in VAD and on-device STT inference.

Installation

Python

pip install genshiai-stt

Node.js

npm install @genshiai/stt

The correct native addon for your platform is installed automatically via optional dependencies.

Browser

npm install @genshiai/stt-web

Quick Start

Python

import asyncio

from genshi_stt import GenshiSTTClient

async def main() -> None:
    async with GenshiSTTClient(api_key="gw-...") as client:
        with open("recording.wav", "rb") as f:
            result = await client.transcribe(f.read())
        print(result.text)
        for seg in result.segments:
            print(f"[{seg.start:.2f}-{seg.end:.2f}] {seg.text}")

        async with client.stream(
            domain="medical",
            secure=True,
            effort="normal",
            dictionary_ids=["dict_hospital"],
            dictionaries=[
                {
                    "id": "dict_hospital",
                    "name": "院内用語",
                    "industry": "medical",
                    "terms": [{"term": "GENSHI AI", "reading": "げんしえーあい"}],
                }
            ],
        ) as session:
            partials = await session.push(audio_chunk)  # PCM16 bytes
            print(partials[0].text if partials else "")

            refined = await session.drain_events()
            for event in refined:
                if event.type == "refined":
                    print(event.index, event.text)

            final = await session.finalize()
            print(final.text)

asyncio.run(main())

Node.js / TypeScript

import { GenshiSTTClient } from '@genshiai/stt';

const client = new GenshiSTTClient({ apiKey: 'gw-...' });

// Batch transcription
const result = await client.transcribe(audioBuffer);
console.log(result.text);
for (const seg of result.segments) {
  console.log(`[${seg.start.toFixed(2)}-${seg.end.toFixed(2)}] ${seg.text}`);
}

// Realtime streaming
const session = client.stream({
  domain: 'medical',
  secure: true,
  effort: 'normal',
  dictionaryIds: ['dict_hospital'],
  dictionaries: [
    {
      id: 'dict_hospital',
      name: '院内用語',
      industry: 'medical',
      terms: [{ term: 'GENSHI AI', reading: 'げんしえーあい' }],
    },
  ],
});
const partials = await session.push(pcm16Chunk);
console.log(partials[0]?.text);

const refined = await session.drainEvents();
for (const event of refined) {
  if (event.type === 'refined') {
    console.log(event.index, event.text);
  }
}

const final = await session.finalize();
console.log(final.text);

Browser

import { GenshiSTTClient, createMicStream } from '@genshiai/stt-web';

const client = new GenshiSTTClient({ apiKey: 'gw-...' });
await client.init();

const session = client.stream({
  domain: 'medical',
  secure: true,
  effort: 'normal',
  dictionaryIds: ['dict_hospital'],
  dictionaries: [
    {
      id: 'dict_hospital',
      name: '院内用語',
      industry: 'medical',
      terms: [{ term: 'GENSHI AI', reading: 'げんしえーあい' }],
    },
  ],
});

const mic = await createMicStream();
mic.onAudio(async (chunk) => {
  const partials = await session.push(chunk);
  console.log(partials[0]?.text);

  const refined = await session.drainEvents();
  for (const event of refined) {
    if (event.type === 'refined') {
      console.log(event.index, event.text);
    }
  }
});

// When done:
const result = await session.finalize();
console.log(result.text);

Prefer await session.finalize() when you need the final corrected text. await session.close() now performs a best-effort finalize for cleanup. Use session.abort() only for intentional force-abort without billing finalize.

Choosing A Mode

Mode During recording Correction cadence Recommended for
batch Nothing is emitted until the request finishes One final full-text pass File upload, post-processing
realtime + effort="normal" partial text appears immediately Background correction is sparse Dictation, meeting notes, standard live input
realtime + effort="high" partial text appears immediately Background correction is more frequent Live captions, simultaneous charting, terminology-sensitive input

Realtime Mental Model

  • push() returns immediate partial events from local STT
  • drain_events() / drainEvents() returns queued refined / error events from background correction
  • effort: "normal" batches corrections sparsely, effort: "high" refines more often
  • finalize() still performs the final full-text correction pass

secure=True / secure: true requests the Secure tier. Secure requests require an industry domain such as medical, or inline custom dictionaries. Public SDK configuration is intentionally centered on domain, secure, dictionaryIds / dictionaries, and effort. Low-level VAD and local model tuning are not part of the public API.

Realtime Event Example

push() returns a partial event:

{
  "type": "partial",
  "text": "ほんじつのけつあつは130の80です。",
  "index": 0,
  "processing_time_ms": 0
}

drain_events() / drainEvents() returns a refined event for the same segment:

{
  "type": "refined",
  "text": "本日の血圧は130の80です。",
  "index": 0,
  "processing_time_ms": 88
}

Response

{
  "text": "本日の血圧は130の80です。次の患者さんをお願いします。",
  "processing_time_ms": 142,
  "segments": [
    {
      "id": 0,
      "start": 0.32,
      "end": 2.15,
      "text": "本日の血圧は130の80です。"
    },
    {
      "id": 1,
      "start": 3.2,
      "end": 4.8,
      "text": "次の患者さんをお願いします。"
    }
  ]
}

Supported Platforms

Platform Python Node.js
macOS ARM64 (Apple Silicon) genshiai-stt-native @genshiai/stt-native-darwin-arm64
Linux x64 genshiai-stt-native @genshiai/stt-native-linux-x64
Windows x64 genshiai-stt-native @genshiai/stt-native-windows-x64
Browser @genshiai/stt-web

Requirements

  • Python >= 3.10 / Node.js >= 20
  • Valid GENSHI Works API key
  • ffmpeg (Node.js batch decode only)

Browser SDK note:

  • await client.init() is required before transcribe() or realtime()
  • the npm package includes JSON metadata, and secured ONNX assets are fetched via POST /v1/activate

Documentation

Full documentation: https://docs.genshi.ai/stt

License

Proprietary. Copyright (c) 2026 GENSHI Works Inc. All rights reserved. See LICENSE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

genshiai_stt-3.0.0.tar.gz (10.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

genshiai_stt-3.0.0-py3-none-any.whl (12.6 kB view details)

Uploaded Python 3

File details

Details for the file genshiai_stt-3.0.0.tar.gz.

File metadata

  • Download URL: genshiai_stt-3.0.0.tar.gz
  • Upload date:
  • Size: 10.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for genshiai_stt-3.0.0.tar.gz
Algorithm Hash digest
SHA256 54cdcbfcc834ca7c176824abbf5ab295fd69a2edbf4d0ed1fcc95985bcc66194
MD5 efd6810daa28daee223ebfd536727bb7
BLAKE2b-256 7ac6b244a12c72104276fc93757438a4b4d528d8e05fe04420f379e3f80a2cad

See more details on using hashes here.

File details

Details for the file genshiai_stt-3.0.0-py3-none-any.whl.

File metadata

  • Download URL: genshiai_stt-3.0.0-py3-none-any.whl
  • Upload date:
  • Size: 12.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for genshiai_stt-3.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ff6e53bf6b769545e86acfda79ecd9b0e238e902b3dacdefd12ed5c519ec930b
MD5 d90f2a2f187e9d6c2801cc9634e57e1d
BLAKE2b-256 7c0055297b7ca31616302a96106cb4c8cb9a30ffe4cd675567c72c9c5ef6e6e6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page