Skip to content

[AI Endpoints] - Audio documentation - Fix typos #8475

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Oct 3, 2025
Merged

[AI Endpoints] - Audio documentation - Fix typos #8475

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
09d7ce0
[AI Endpoints] - Audio documentation - Fix typos
MathieuBsqt Oct 2, 2025
f5ef6e1
Duplication
Y0Coss Oct 2, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
116 changes: 96 additions & 20 deletions ...loud/ai_machine_learning/endpoints_guide_08_audio_transcriptions/guide.en-gb.md
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
---
title: AI Endpoints - Speech to Text
excerpt: Learn how to transcribe audio files with OVHcloud AI Endpoints
updated: 2025-10-01
updated: 2025-10-03
---

> [!primary]
Expand All @@ -15,7 +15,7 @@ updated: 2025-10-01

**Speech to Text** is a powerful feature that enables the conversion of spoken language into written text.

The Speech to Text endpoints on AI Endpoints allow you to easily integrate this technology into your applications, enabling you to transcribe audio files with high accuracy. Our endpoints support various audio formats and provide flexible configuration options to suit your specific use cases.
The Speech to Text APIs on AI Endpoints allow you to easily integrate this technology into your applications, enabling you to transcribe audio files with high accuracy. Our endpoints support various [audio formats](#parameters-overview) and provide flexible configuration options to suit your specific use cases.

## Objective

Expand Down Expand Up @@ -48,7 +48,7 @@ The examples provided during this guide can be used with one of the following en
>> A standard terminal, with [cURL](https://cURL.se/) installed on the system.
>>

*These exmaples will be using the [Whisper-large-v3](https://endpoints.ai.cloud.ovh.net/models/whisper-large-v3) model.*
*These examples will be using the [Whisper-large-v3](https://endpoints.ai.cloud.ovh.net/models/whisper-large-v3) model.*

## Authentication & Rate Limiting

Expand All @@ -66,7 +66,7 @@ The request body for the audio transcription endpoint is of type `multipart/form
|--------------------------|----------|---------------|---------------------------------------------------------------------------------------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **file** | Yes | binary | `mp3`, `mp4`, `aac`, `m4a`, `wav`, `flac`, `ogg`, `opus`, `webm`, `mpeg`, `mpga` | - | The **audio file object (not file name)** to transcribe. |
| **chunking_strategy** | No | `string`/`server_vad object`/`null` | - | null | Strategy for dividing the audio into chunks. More details [here](#chunking-strategy). |
| **diarize** | No | `boolean`/`null` | `true`/`false` | false | Enables speaker separation in the transcript. When set to true, the system separates the audio into segments based on speakers, by adding labels like "Speaker 1" and "Speaker 2", so you can see who said what in conversations such as interviews, meetings, or phone calls. More details [here](#diarize). |
| **diarize** | No | `boolean`/`null` | `true`/`false` | false | Enables speaker separation in the transcript. When set to true, the system separates the audio into segments based on speakers, by adding labels like "Speaker 1" and "Speaker 2", so you can see who said what in conversations such as interviews, meetings, or phone calls. More details [here](#diarization). |
| **language** | No | `string`/`null` | [ISO-639-1 format](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes) | - | The language parameter specifies the language spoken in the input audio. Providing it can improve transcription accuracy and reduce latency (e.g. `en` for English, `fr` for French, `de` for German, `es` for Spanish, `zh` for Chinese, `ar` for Arabic ...). If not provided, the system will attempt automatic language detection, which may be slightly slower and less accurate in some cases. [More details on language compatibility and performance](#language-compatibility-and-performances). |
| **model** | No | `string`/`null` | ID of the model to use | - | Specifies the model to use for transcription. Useful when using our [unified endpoint](/pages/public_cloud/ai_machine_learning/endpoints_guide_07_virtual_models). |
| **prompt** | No | `string`/`null` | - | - | Text to guide the model's style, translate transcript to english or continue a previous audio segment. The language in which you write the prompt must match the audio's one. More details about prompt usage [here](#prompt). |
Expand Down Expand Up @@ -314,7 +314,7 @@ The `prompt` parameter lets you provide extra context to improve transcription.
>> }
>> ```
>>
>> **Translating transcript into English**
> **Translating transcript into English**
>>
>> To directly translate the transcription into English instead of keeping it in the source language, you can pass the special translation token `<|translate|>` in your prompt:
>>
Expand Down Expand Up @@ -401,8 +401,39 @@ The `timestamp_granularities` parameter controls the level of time markers inclu
>> ```json
>> words=[],
>> segments=[
>> {'id': 1, 'seek': 0, 'start': 1.76, 'end': 4.58, 'text': ' France is the world's leading tourist destination', 'tokens': [50365, 1456, 1181, 650, 1459, 1030, 476, 8124, 515, 50609], 'temperature': 0.0, 'avg_logprob': -0.14139344, 'compression_ratio': 1.2769231, 'no_speech_prob': 0.007171631},
>> {'id': 2, 'seek': 0, 'start': 9.44, 'end': 14.92, 'text': ' having received 100 million foreign visitors in 2023.', 'tokens': [50609, 4042, 25011, 3925, 650, 1459, 11, 1022, 517, 594, 2672, 14303, 11, 2064, 1001, 465, 1872, 312, 66, 517, 490, 609, 13, 51117], 'temperature': 0.0, 'avg_logprob': -0.14139344, 'compression_ratio': 1.2769231, 'no_speech_prob': 0.007171631},
>> {
>> 'id': 1,
>> 'seek': 0,
>> 'start': 1.76,
>> 'end': 4.58,
>> 'text': ' France is the world's leading tourist destination',
>> 'tokens': [
>> 50365,
>> 1456,
>> 1181,
>> ...
>> ],
>> 'temperature': 0.0, 'avg_logprob': -0.14139344,
>> 'compression_ratio': 1.2769231,
>> 'no_speech_prob': 0.007171631
>> },
>> {
>> 'id': 2,
>> 'seek': 0,
>> 'start': 9.44,
>> 'end': 14.92,
>> 'text': 'having received 100 million foreign visitors in 2023.',
>> 'tokens': [
>> 50609,
>> 4042,
>> 25011,
>> ...
>> ],
>> 'temperature': 0.0,
>> 'avg_logprob': -0.14139344,
>> 'compression_ratio': 1.2769231,
>> 'no_speech_prob': 0.007171631
>> },
>> ...
>> ]
>> ```
Expand Down Expand Up @@ -454,8 +485,39 @@ The `timestamp_granularities` parameter controls the level of time markers inclu
>> ...
>> ],
>> segments=[
>> {'id': 1, 'seek': 0, 'start': 1.76, 'end': 4.58, 'text': ' France is the world's leading tourist destination', 'tokens': [50365, 1456, 1181, 650, 1459, 1030, 476, 8124, 515, 50609], 'temperature': 0.0, 'avg_logprob': -0.14139344, 'compression_ratio': 1.2769231, 'no_speech_prob': 0.007171631},
>> {'id': 2, 'seek': 0, 'start': 9.44, 'end': 14.92, 'text': ' having received 100 million foreign visitors in 2023.', 'tokens': [50609, 4042, 25011, 3925, 650, 1459, 11, 1022, 517, 594, 2672, 14303, 11, 2064, 1001, 465, 1872, 312, 66, 517, 490, 609, 13, 51117], 'temperature': 0.0, 'avg_logprob': -0.14139344, 'compression_ratio': 1.2769231, 'no_speech_prob': 0.007171631},
>> {
>> 'id': 1,
>> 'seek': 0,
>> 'start': 1.76,
>> 'end': 4.58,
>> 'text': ' France is the world's leading tourist destination',
>> 'tokens': [
>> 50365,
>> 1456,
>> 1181,
>> ...
>> ],
>> 'temperature': 0.0, 'avg_logprob': -0.14139344,
>> 'compression_ratio': 1.2769231,
>> 'no_speech_prob': 0.007171631
>> },
>> {
>> 'id': 2,
>> 'seek': 0,
>> 'start': 9.44,
>> 'end': 14.92,
>> 'text': 'having received 100 million foreign visitors in 2023.',
>> 'tokens': [
>> 50609,
>> 4042,
>> 25011,
>> ...
>> ],
>> 'temperature': 0.0,
>> 'avg_logprob': -0.14139344,
>> 'compression_ratio': 1.2769231,
>> 'no_speech_prob': 0.007171631
>> },
>> ...
>> ]
>> ```
Expand Down Expand Up @@ -520,6 +582,7 @@ The `response_format` determines how the transcription data is returned. Availab
>> "duration": 5
>> }
>> }
>> ```
>>
> **Text**
>>
Expand All @@ -530,11 +593,11 @@ The `response_format` determines how the transcription data is returned. Availab
>>
> **SRT**
>>
>> Not yet supported.
>> **Not yet supported.**
>>
> **VTT**
>>
>> Not yet supported.
>> **Not yet supported.**
>>

#### Chunking Strategy
Expand Down Expand Up @@ -577,7 +640,7 @@ However, transcription quality and speed depend on the **language of the input a
- Less common or low-resource languages may yield lower accuracy or longer processing times.
- Regional accents, dialects, or code-switching (switching between multiple languages in the same recording) can reduce accuracy further.

Providing the `language` parameter explicitly (instead of relying on automatic detection) generally improves both accuracy and latency.
Providing the `language` parameter explicitly (instead of relying on automatic detection) generally improves both accuracy and latency. Expected format is [ISO-639-1 format](https://en.wikipedia.org/wiki/List_of_ISO_639_language_codes) (e.g. `en` for English, `fr` for French, `de` for German, `es` for Spanish, `zh` for Chinese, `ar` for Arabic ...).

For a detailed performance breakdown by language, see [Whisper’s benchmark results](https://github.com/openai/whisper?tab=readme-ov-file#available-models-and-languages). This includes word error rates (WER) and character error rates (CER) across different datasets.

Expand Down Expand Up @@ -610,29 +673,42 @@ Try to avoid splitting mid-sentence, as this can cause context to be lost and re

**Example**

Splitting Audio with open-source Python PyDub library:
Splitting Audio with open-source Python `pydub` library:

```python
from pydub import AudioSegment
import math
import os

# Load the audio file
audio = AudioSegment.from_mp3("long_interview.mp3")

# Define chunk duration in milliseconds (e.g., 10 minutes)
chunk_duration = 10 * 60 * 1000
# Define chunk duration in milliseconds (e.g., 30 minutes)
chunk_duration = 30 * 60 * 1000 # 30 minutes

# Calculate how many chunks we need
num_chunks = math.ceil(len(audio) / chunk_duration)

# Ensure output folder exists
output_dir = "chunks"
os.makedirs(output_dir, exist_ok=True)

# Split first chunk
first_chunk = audio[:chunk_duration]
# Loop through and export each chunk
for i in range(num_chunks):
start_time = i * chunk_duration
end_time = min((i + 1) * chunk_duration, len(audio))
chunk = audio[start_time:end_time]

# Export chunk
first_chunk.export("long_interview_part1.mp3", format="mp3")
chunk_filename = os.path.join(output_dir, f"long_interview_part{i+1}.mp3")
chunk.export(chunk_filename, format="mp3")
print(f"Exported {chunk_filename}")
```

Repeat this process to create multiple chunks, then transcribe each chunk individually.

> [!warning]
>
> OVHcloud makes no guarantees about the usability or security of third-party software like PyDub.
> OVHcloud makes no guarantees about the usability or security of third-party softwares like `pydub`.

## Conclusion

Expand Down
Loading