Speech-to-text

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Models

• Qwen3-ASR is Qwen’s multilingual model. The 1.7B model is more accurate than the 0.6B. Its word timestamps come from a separate forced aligner that doesn’t cover every language: a request for word timestamps in a language it doesn’t cover returns a 400, and the same request without word timestamps succeeds.
• Whisper Large v3 Turbo is OpenAI’s multilingual model, a faster version of Whisper Large v3. Its words start with a space and carry a probability.
• Parakeet TDT 0.6B v3 is NVIDIA’s fast multilingual model, with word timestamps from the model itself. It detects the language on its own, ignores language and temperature, and reports language as null.
• Parakeet Ultra is Moondream’s fine-tune of Parakeet TDT 0.6B v3, more accurate at the same speed. It also ignores language and temperature.
• Nemotron 3.5 Transcribe is NVIDIA’s 30B mixture-of-experts model, with 4B parameters active per token. It reports language as null.

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Use Prime Inference

export PRIME_API_KEY="your-api-key"

curl https://api.pinference.ai/api/v1/audio/transcriptions \
  -H "Authorization: Bearer $PRIME_API_KEY" \
  -F model=qwen/qwen3-asr-1.7b \
  -F response_format=verbose_json \
  -F "timestamp_granularities[]=segment" \
  -F "timestamp_granularities[]=word" \
  -F file=@interview.mp3

import os
from openai import OpenAI

client = OpenAI(base_url="https://api.pinference.ai/api/v1", api_key=os.environ["PRIME_API_KEY"], timeout=600)
with open("interview.mp3", "rb") as f:
    transcript = client.audio.transcriptions.create(model="qwen/qwen3-asr-1.7b", file=f)
print(transcript.text)

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Transcribe long recordings

curl https://api.pinference.ai/api/v1/audio/transcriptions \
  -H "Authorization: Bearer $PRIME_API_KEY" \
  -H "Content-Type: application/json" \
  --max-time 600 \
  -d '{
    "model": "qwen/qwen3-asr-1.7b",
    "audio_url": "https://storage.example.com/recordings/all-hands.m4a?signature=...",
    "response_format": "verbose_json"
  }'