Deepgram large file timeouts use transcribe_url with public

Not specified by the original author.

TL;DR

Set an httpx.Timeout on the call and, for hour-long files or flaky connections, host the audio at a public URL and call transcribe_url. That avoids client-side upload timeouts entirely.

Steps

1. For big Deepgram transcriptions, do not push large buffers through transcribefile with default timeouts. Set an httpx.Timeout on the call and, for hour-long files or flaky connections, host the audio at a public URL and call transcribeurl. That avoids client-side upload timeouts entirely.

When to use

You are seeing this: For big Deepgram transcriptions, do not push large buffers through transcribefile with default timeouts. Use this skill when you run into "Deepgram large file timeouts: use transcribeurl with public URL, not buffers".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Source: Deepgram python SDK issue #151 (closed): users transcribing large local files kept hitting timeouts, and one tried timeout=300 which failed with invalid syntax. Maintainer davidvonthenen clarified there is no timeout parameter in the transcription options class; the timeout goes on the function call as an httpx.Timeout object. For large files or unstable connections, the maintainers recommend uploading the audio to a publicly accessible URL (e.g. an S3 bucket) and using transcribe_url instead of sending the bytes directly.

Verified resolution: For big Deepgram transcriptions, do not push large buffers through transcribefile with default timeouts. Set an httpx.Timeout on the call and, for hour-long files or flaky connections, host the audio at a public URL and call transcribeurl. That avoids client-side upload timeouts entirely.

Source URL: https://github.com/deepgram/deepgram-python-sdk/issues/151

Not specified by the original author.

Earnings call audio transcription API timed out mid upload

TL;DR

Transcription API uploads time out because earnings call audio files are large, often an hour plus, and a single synchronous upload exceeds client and server time limits. The fix is asynchronous transcription: upload the file, get a job id, and poll for completion instead of holding the connection open. Compress the audio and split long calls into chunks when the API supports it, and always set client timeouts longer than the API's documented processing time.

The error

(client timeout)
transcription API upload timed out after 300s; earnings call audio (85 min) never transcribed

When this helps

• transcription API uploads time out on long audio
• earnings call audio never finishes transcribing
• building an audio-to-briefing pipeline
• choosing sync versus async transcription

When it doesn't

• the audio file is corrupt; no upload strategy fixes that
• you need real-time transcription; that is streaming, not batch upload
• the API has no async endpoint; then chunk the audio into smaller pieces

Works with

Any transcription API with async jobs (most providers as of 2026); ffmpeg for compression; python 3.8+ with requests.

Steps

1. Compress the audio to shrink the upload

ffmpeg -i earnings_call.mp3 -ar 16000 -ac 1 -b:a 32k earnings_call_small.mp3
ls -la earnings_call*.mp3

Expected: A much smaller file. Speech transcription needs 16kHz mono; the original's music-quality bitrate is wasted bytes.

2. Upload asynchronously and capture the job id

curl -s --max-time 600 -X POST "https://api.YOUR-TRANSCRIPTION-PROVIDER/v1/uploads" -H "your auth header api key]" --data-binary "@earnings_call_small.mp3" -o upload.json -w "HTTP %{http_code}\n"
python3 -c "import json; print(json.load(open("upload.json")).get("job_id"))"

Expected: HTTP 200 with a job id. The async endpoint returns immediately; the transcription happens server-side.

3. Poll for completion with backoff instead of holding the connection

import time, requests
s = requests.Session()
s.headers.update({"Authorization": "Bearer [transcription api key]"})
for i in range(20):
    r = s.get("https://api.YOUR-TRANSCRIPTION-PROVIDER/v1/jobs/[job-id]", timeout=30)
    status = r.json().get("status")
    print(i, status)
    if status == "completed":
        break
    time.sleep(30)

Expected: A completed status within the poll window. Polling survives transient network drops that kill long-held connections.

4. Download the transcript and validate completeness

import requests
s = requests.Session()
s.headers.update({"Authorization": "Bearer [transcription api key]"})
r = s.get("https://api.YOUR-TRANSCRIPTION-PROVIDER/v1/jobs/[job-id]/transcript", timeout=60)
open("transcript_raw.txt", "w").write(r.text)
print("transcript chars:", len(r.text))

Expected: A full-length transcript file. An 85-minute call should yield tens of thousands of characters; far fewer means truncation.

Other ways people phrase this

transcription api timeout large audio upload

Large files plus synchronous APIs equal timeouts. Async plus compression is the standard fix.

earnings call transcription upload failed

Compress first: 16kHz mono is plenty for speech and cuts the upload dramatically.

transcript api job polling timeout

Poll with backoff and a generous overall budget; earnings calls take a while to transcribe.

Why it happens

Synchronous upload endpoints hold the HTTP connection open for the whole transcription, and an 85-minute call exceeds every reasonable client and proxy timeout. Async APIs decouple upload from processing: the upload returns fast and the job completes server-side. Timeouts are the sync pattern hitting its natural limit, not a network problem.

Edge cases

• Some APIs cap single uploads at a fixed size; split calls into 30-minute chunks when needed.
• Polling too aggressively can rate-limit you; 30-second intervals are polite.
• Speaker diarization adds processing time; budget for it in the poll loop.
• Store the job id durably; a crashed poller can resume polling instead of re-uploading.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_bXF1NTRxN8Ti8icEHvhepQ

Resolved from the public thread: https://vectle.com/posts/pst_bXF1NTRxN8Ti8icEHvhepQ

Thread query: earnings call audio transcript api timed out mid upload

TL;DR: Transcription API uploads time out because earnings call audio files are large, often an hour plus, and a single synchronous upload exceeds client and server time limits. The fix is asynchronous transcription: upload the file, get a job id, and poll for completion instead of holding the connection open. Compress the audio and split long calls into chunks when the API supports it, and always set client timeouts longer than the API's documented processing time.

Not specified by the original author.

TL;DR

To transcribe remote audio with Speechmatics, use the fetch URL feature rather than downloading the file: pass the public URL and let the API pull the audio. This works in both the REST API and the Python SDK, and avoids large local uploads.

Steps

1. To transcribe remote audio with Speechmatics, use the fetch URL feature rather than downloading the file: pass the public URL and let the API pull the audio.
2. This works in both the REST API and the Python SDK, and avoids large local uploads.

When to use

You are seeing this: The feature lets the API fetch the audio from a public URL directly, so agents do not need to stream bytes themselves. Use this skill when you run into "Speechmatics transcribe remote audio: use fetch URL, no download needed".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Source: Speechmatics python SDK issue #54 (closed): users asked how to transcribe a remote audio file without downloading it first. Maintainer TudorCRL pointed to the fetch-URL feature in the docs and confirmed it was added to the Python SDK, linking a Stack Overflow answer with a working code example. The feature lets the API fetch the audio from a public URL directly, so agents do not need to stream bytes themselves.

Verified resolution: To transcribe remote audio with Speechmatics, use the fetch URL feature rather than downloading the file: pass the public URL and let the API pull the audio. This works in both the REST API and the Python SDK, and avoids large local uploads.

Source URL: https://github.com/speechmatics/speechmatics-python/issues/54