APAC Speech-to-Text and Transcription Guide 2026: Gladia, Speechmatics, and WhisperX

APAC Speech-to-Text: Matching Transcription Infrastructure to Workload

APAC AI teams need speech-to-text for three distinct workloads: production API transcription with speaker diarization for meeting intelligence and call analytics, enterprise on-premise ASR for regulated APAC industries that cannot use cloud providers, and offline batch transcription at zero API cost for large audio archives. This guide covers the transcription platforms APAC teams use for each workload type.

Gladia — cloud STT API with real-time streaming, speaker diarization, automatic translation, and audio intelligence for APAC meeting and call analytics.

Speechmatics — enterprise ASR with 50+ language support including Mandarin, Japanese, and ASEAN languages, and on-premise Docker/K8s deployment for APAC data sovereignty.

WhisperX — open-source Whisper enhancement with speaker diarization, word-level timestamps, and VAD filtering for APAC on-premise GPU transcription at zero API cost.

APAC Speech-to-Text Selection Framework

APAC Workload                          → Platform       → Why

Meeting intelligence platform          → Gladia         Speaker diarization +
(cloud, needs diarization + summary)   →                audio intelligence

Call analytics (cloud)                 → Deepgram/Gladia Real-time streaming;
(live agent monitoring)                →                per-minute pricing

Regulated industry ASR                 → Speechmatics   On-premise; data
(FSI/health, no cloud audio)           →                sovereignty; APAC SLA

Large audio archive transcription      → WhisperX       Zero API cost;
(batch, GPU available, offline OK)     →                GPU faster-than-realtime

Multilingual APAC → English            → Gladia         Translation combined
(single-step translate + transcribe)   →                with transcription

Custom APAC domain vocabulary          → Speechmatics   Custom vocabulary
(regulatory/medical/financial terms)   →                training and tuning

APAC Transcription Cost (indicative, per 60-minute audio):
  Deepgram Nova-2:     $0.36      (cloud, fast, good for EN/APAC)
  Gladia:              $0.72      (cloud, with diarization + translation)
  AssemblyAI:          $0.37      (cloud, with speaker labels)
  Speechmatics:        Enterprise (on-premise, per-seat or volume)
  WhisperX on RunPod:  ~$0.03     (RTX 4090 spot × 4min compute time)

Gladia: APAC Meeting Intelligence Transcription

Gladia APAC async transcription with diarization

# APAC: Gladia — transcribe meeting audio with speaker diarization

import requests
import time

GLADIA_API_KEY = os.environ["GLADIA_API_KEY"]

# APAC: Upload audio file for async transcription
with open("apac_board_meeting_2026_05_28.mp3", "rb") as apac_audio:
    apac_upload = requests.post(
        "https://api.gladia.io/v2/upload",
        headers={"x-gladia-key": GLADIA_API_KEY},
        files={"audio": apac_audio},
    )
apac_audio_url = apac_upload.json()["audio_url"]

# APAC: Request transcription with diarization and translation
apac_transcription_request = requests.post(
    "https://api.gladia.io/v2/pre-recorded",
    headers={"x-gladia-key": GLADIA_API_KEY, "Content-Type": "application/json"},
    json={
        "audio_url": apac_audio_url,
        "diarization": True,           # APAC: speaker separation
        "diarization_config": {
            "number_of_speakers": 6,   # APAC: board meeting with 6 participants
        },
        "translation": True,
        "translation_config": {
            "target_languages": ["en"],  # APAC: Mandarin/JP source → EN
        },
        "summarization": True,
        "summarization_config": {
            "type": "bullet_points",   # APAC: board summary as bullet list
        },
        "named_entity_recognition": True,  # APAC: extract company/person/regulation names
    },
)
apac_result_url = apac_transcription_request.json()["result_url"]

# APAC: Poll for completion
while True:
    apac_result = requests.get(
        apac_result_url,
        headers={"x-gladia-key": GLADIA_API_KEY},
    ).json()
    if apac_result["status"] == "done":
        break
    time.sleep(5)

# APAC: Process diarized transcript
for utterance in apac_result["result"]["transcription"]["utterances"]:
    speaker = utterance["speaker"]       # APAC: "speaker_0", "speaker_1" etc.
    start = utterance["start"]           # seconds
    text = utterance["transcript"]
    print(f"[{start:.1f}s] Speaker {speaker}: {text}")

# APAC: Board meeting summary
apac_summary = apac_result["result"]["summarization"]["results"]
print("\nAPAC Board Meeting Summary:\n" + apac_summary)

Gladia APAC real-time streaming transcription

# APAC: Gladia — real-time streaming for live APAC call transcription

import asyncio
import websockets
import json
import base64

async def apac_live_transcription(apac_audio_stream):
    """APAC: Stream audio to Gladia and receive partial transcripts in real-time."""

    # APAC: Open WebSocket to Gladia streaming endpoint
    async with websockets.connect(
        "wss://api.gladia.io/audio/text/audio-transcription",
        extra_headers={"x-gladia-key": os.environ["GLADIA_API_KEY"]},
    ) as apac_ws:

        # APAC: Configure streaming session
        await apac_ws.send(json.dumps({
            "x_gladia_key": os.environ["GLADIA_API_KEY"],
            "frames_format": "bytes",
            "encoding": "WAV/PCM",
            "bit_depth": 16,
            "sample_rate": 16000,
            "language": "en",         # APAC: or "zh" for Mandarin real-time
            "diarization": True,
        }))

        # APAC: Stream audio chunks and print partial results
        async def apac_send_audio():
            async for apac_chunk in apac_audio_stream:
                await apac_ws.send(apac_chunk)

        async def apac_receive_transcripts():
            async for apac_message in apac_ws:
                apac_data = json.loads(apac_message)
                if apac_data.get("event") == "transcript":
                    apac_text = apac_data.get("transcription", "")
                    apac_is_final = apac_data.get("type") == "final"
                    if apac_is_final:
                        print(f"APAC FINAL: {apac_text}")
                    # APAC: Partial results appear within 200ms for live captions

        await asyncio.gather(apac_send_audio(), apac_receive_transcripts())

Speechmatics: APAC On-Premise Enterprise ASR

Speechmatics APAC self-hosted deployment

# APAC: Speechmatics — on-premise Docker deployment for data sovereignty

# APAC: Pull Speechmatics runtime container (requires enterprise license)
docker pull speechmatics/runtime:latest

# APAC: Start Speechmatics runtime with APAC language models
docker run -d \
  --name apac-speechmatics \
  --gpus all \
  -p 9000:9000 \
  -v /apac-models:/models \
  -e SM_LICENSE_KEY=${SM_LICENSE_KEY} \
  speechmatics/runtime:latest \
  --model-path /models/en-SG \     # APAC: Singapore English model
  --model-path /models/zh-CN \     # APAC: Mandarin Simplified model
  --model-path /models/ja-JP       # APAC: Japanese model

# APAC: Audio never leaves your APAC data center
# → MAS, HKMA, PDPA, APPI compliance for sensitive call recordings

Speechmatics APAC transcription API call

# APAC: Speechmatics — transcribe APAC call recording on-premise

import httpx

# APAC: On-premise Speechmatics endpoint (not cloud)
APAC_SPEECHMATICS_URL = "http://apac-speechmatics.internal:9000"

async def apac_transcribe_call(apac_audio_path: str, apac_language: str = "en") -> dict:
    """APAC: Transcribe call recording on self-hosted Speechmatics."""

    async with httpx.AsyncClient(base_url=APAC_SPEECHMATICS_URL) as apac_client:

        # APAC: Submit transcription job
        with open(apac_audio_path, "rb") as apac_f:
            apac_job = await apac_client.post(
                "/v1/jobs",
                files={"data_file": apac_f},
                data={
                    "config": json.dumps({
                        "type": "transcription",
                        "transcription_config": {
                            "language": apac_language,  # "en", "zh", "ja", "ko"
                            "diarization": "speaker",
                            "enable_entities": True,    # APAC: extract org/person names
                            "output_locale": apac_language,
                            "additional_vocab": [
                                # APAC: domain-specific terms Speechmatics should recognize
                                {"content": "FEAT", "sounds_like": ["feat"]},
                                {"content": "MAS", "sounds_like": ["mass", "M-A-S"]},
                                {"content": "PDPA", "sounds_like": ["P-D-P-A"]},
                            ],
                        },
                    })
                },
            )
        apac_job_id = apac_job.json()["id"]

        # APAC: Wait for completion and retrieve transcript
        while True:
            apac_status = await apac_client.get(f"/v1/jobs/{apac_job_id}")
            if apac_status.json()["job"]["status"] == "done":
                break
            await asyncio.sleep(2)

        apac_transcript = await apac_client.get(
            f"/v1/jobs/{apac_job_id}/transcript",
            params={"format": "json-v2"},
        )
        return apac_transcript.json()

WhisperX: APAC Open-Source Batch Transcription

WhisperX APAC meeting transcription with diarization

# APAC: WhisperX — offline batch transcription with speaker diarization

import whisperx
import torch

# APAC: Load WhisperX model (large-v3 for APAC multilingual accuracy)
apac_device = "cuda"
apac_model = whisperx.load_model(
    "large-v3",
    device=apac_device,
    compute_type="float16",    # APAC: float16 for RTX 4090 / A100 efficiency
    language="en",             # APAC: or None for auto-detect (slower)
)

# APAC: Load and transcribe meeting audio
apac_audio = whisperx.load_audio("apac_team_meeting_2026_05_28.mp3")
apac_result = apac_model.transcribe(
    apac_audio,
    batch_size=16,             # APAC: adjust for GPU VRAM (16 for 24GB RTX 4090)
)

# APAC: Align timestamps to word level (requires alignment model)
apac_align_model, apac_metadata = whisperx.load_align_model(
    language_code=apac_result["language"],
    device=apac_device,
)
apac_result = whisperx.align(
    apac_result["segments"],
    apac_align_model,
    apac_metadata,
    apac_audio,
    apac_device,
)

# APAC: Diarize speakers (requires pyannote.audio HuggingFace token)
apac_diarize_model = whisperx.DiarizationPipeline(
    use_auth_token=os.environ["HF_TOKEN"],
    device=apac_device,
)
apac_diarize_segments = apac_diarize_model(
    apac_audio,
    min_speakers=2,
    max_speakers=8,   # APAC: team meeting upper bound
)
apac_result = whisperx.assign_word_speakers(apac_diarize_segments, apac_result)

# APAC: Output: word-level timestamps + speaker attribution
for segment in apac_result["segments"]:
    speaker = segment.get("speaker", "UNKNOWN")
    text = segment["text"]
    start = segment["start"]
    print(f"[{start:.1f}s] {speaker}: {text}")

# APAC: Cost: ~4 minutes on RTX 4090 for 60-minute meeting = ~$0.03 RunPod spot
# vs Gladia: ~$0.72 for same audio

Related APAC Speech AI Resources

For the voice AI phone agent platforms (Vapi, Retell AI, Bland AI) that use Gladia and Deepgram as their real-time STT backbone — integrating speech recognition into outbound and inbound AI phone call automation — see the APAC voice AI and phone agent guide.

For the GPU cloud platforms (RunPod, DeepInfra) that provide the compute infrastructure for running WhisperX and local Whisper variants on-premise or on rented GPU instances — see the APAC GPU cloud and serverless inference guide.

For the meeting productivity AI tools (tl;dv, Otter.ai) that consume ASR transcripts from these platforms to produce meeting summaries, action items, and searchable meeting libraries — see the APAC AI tools catalog.