Troubleshooting OGG_OPUS Transcription Error | Generated by AI

Home 2025.10 English 中文 日本語 Español हिंदी Français Deutsch العربية 繁體中文

---

It looks like you’re running into an issue with an audio transcription API (likely Google Cloud Speech-to-Text, based on the config details like the language code and Chirp model). The error indicates that the OGG_OPUS audio file isn’t being recognized as a valid supported format, even though OGG_OPUS is generally supported for that service. This could be due to file corruption, incorrect metadata, encoding variations, or how the file is being uploaded/processed. Let’s troubleshoot this step by step.

1. Verify the Audio File

• Check the file integrity: Play the OGG_OPUS file in a media player (e.g., VLC, Audacity, or FFmpeg) to ensure it’s not corrupted. If it doesn’t play correctly, re-encode or re-export the file.
• Inspect metadata: Use a tool like ffprobe (from FFmpeg) to confirm the format:

  ffprobe your_audio_file.ogg
  

  Look for output confirming:

  • Codec: opus
  • Sample rate: 48000 Hz
  • Channels: 1 (mono) If it doesn’t match, the file might be mislabeled.
• File size and duration: Your transcription shows ~9.8 seconds—ensure the file isn’t truncated.

2. Specify Decoding Parameters

As the error suggests, explicitly provide decoding details in your API request. For Google Cloud Speech-to-Text (v2), structure your request like this (using the Node.js client as an example; adapt for your language/SDK):

   const speech = require('@google-cloud/speech').v2;

   const client = new speech.SpeechClient();

   const request = {
     recognizer: 'projects/your-project/locations/us/recognizers/your-recognizer', // Replace with your details
     config: {
       encoding: 'OGG_OPUS',  // Explicitly specify this
       sampleRateHertz: 48000,
       languageCode: 'cmn-Hans-CN',
       model: 'chirp',  // Note: Chirp 3 might be 'latest_short' or similar; confirm in docs
       // Add any other options, e.g., enableAutomaticPunctuation: true
     },
     audio: {
       content: Buffer.from(fs.readFileSync('your_audio_file.ogg')).toString('base64'), // Base64-encode the file
     },
     // If using features, add here
   };

   const [response] = await client.recognize(request);
   console.log(response);

• Key changes:
  • Explicitly set encoding: 'OGG_OPUS', sampleRateHertz: 48000, and channel count implicitly via the file (or if needed, add audioChannelCount: 1).
  • Ensure the audio content is properly base64-encoded if uploading raw bytes.
  • For Chirp 3, double-check the model name in the API docs—it might be chirp_3 or a variant under “models”.

If you’re using a different SDK (e.g., Python, curl), refer to the Google Cloud Speech-to-Text v2 docs for the equivalent config.

3. Convert the File if Needed

OGG_OPUS can sometimes cause issues due to Opus codec quirks. Try converting to a more universally supported format like LINEAR16 (WAV/PCM) using FFmpeg (free tool):

   ffmpeg -i your_audio_file.ogg -ar 48000 -ac 1 -f wav output.wav

• Then update your API config:
  • encoding: 'LINEAR16'
  • sampleRateHertz: 48000
• Re-run the transcription. This often resolves encoding mismatches without losing quality for short clips.

4. Other Common Fixes

• API Version/Region: You’re using v2 in us—that’s fine for Chirp, but ensure your project has the necessary permissions (e.g., Speech-to-Text API enabled) and quota.
• File Upload Method: If uploading via file path or URL, ensure it’s accessible and not modified in transit. For large files, use asynchronous recognition (longRunningRecognize).
• Language/Model Mismatch: cmn-Hans-CN with Chirp 3 should work for Mandarin, but test with a simpler model like latest_long if issues persist.
• Test with a Sample File: Download a known-good OGG_OPUS sample from online (e.g., Wikimedia Commons) and run it through your setup to isolate if it’s file-specific.
• Error Logs: Check the full API response or console logs for more details (e.g., inner error codes). If it’s a quota or auth issue, that’ll show up separately.

If you share more details—like the exact API you’re using (Google Cloud? OpenAI Whisper? Something else?), code snippets, or the full error traceback—I can give more tailored advice. Have you tried any of these steps yet?

---

Back

x-ai/grok-4-fast:free

Donate