Time-based transcriptions of media content, powered by Google's Speech APIs

This service currently lives on Heroku.

This service can transcribe the following media formats:

• mp4
• wav
• mp3
• ogg
• m4a
• mxf

Any files sent to the service for transcription must not be larger than 419430400 bytes (40 mb). If a file is uploaded/a resource is passed to the service, and it is too big, the transcription will not be performed.

There are two ways to start a transcription:

1. You can pass a file to /transcribe as binary data
2. You can pass a URL that points to a valid media file

In order to start a transcription, you must obtain a token to include in your request as a query parameter

Tokens can only be created by FT staff.

To create a token head to /token/generate.

This endpoint is protected by S3O. If authentication is successful, you'll receieve a JSON response containing your token, and your S3O username.

{
	token: "VALID_TOKEN",
	owner: "your.name"
}

For every other endpoint, this token must be included as a query parameter ?token=VALID_TOKEN.

You can upload a file for transcription like so:

curl --request POST --data-binary "@file_to_transcribe.mp3" https://ftlabs-transcription.herokuapp.com/transcribe?token=VALID_TOKEN

You can also point the transcription service to a valid resource on the internet for transcription. These files mist be publically accessible, and are still subject to the same constraints as files that are uploaded for transcription.

You can pass a url pointing to the resource for transcription like so:

curl https://ftlabs-transcription.herokuapp.com/transcribe?resource=[URL_FOR_FILE_TO_BE_TRANSCRIBED]&token=VALID_TOKEN

If the upload is successful, a JSON object will be returned with a stauts, id, and a helpful message about what you can do with the id.

{
	"status": "ok",
	"id" : "VALID_ID"
	"message": "Job created. Please check https://ftlabs-transcription.herokuapp.com/get/VALID_ID to get status/transcription."
}

Transcriptions take time (01:30s per 5 minutes of audio as a rule-of-thumb). You can check the status of the transcription process by visiting:

https://ftlabs-transcription.herokuapp.com/get/YOUR_JOB_ID

If the job is not finished, you'll recieve:

{
	"ID": "YOUR_JOB_ID",
	"finished": false
}

if the job is finished, you'll recieved a JSON object with both the whole transcrition, and the time indexed chunks:

{
	"ID": "ryepEUJYwg",
	"finished": true,
	"transcription": {
		"whole": "This is just a short demo of the transcription service",
		"transcribedChunks": [
			{
				"transcription": "This is just a short",
				"timeOffsets": {
					"start": 0,
					"end": 01.86,
					"duration": 01.86
				}
			},
			{
				"transcription": "demo of the transcription service",
				"timeOffsets": {
					"start": 01.86,
					"end": 03.18,
					"duration": 01.32
				}
			}
		]
	}
}

You can also have the transcription returned as a VTT file by passing adding a query parameter to the GET request.

https://ftlabs-transcription.herokuapp.com/get/YOUR_JOB_ID?output=vtt

This will not trigger a new transcription, it will rearrange the existing data into the VTT format.

WEBVTT

1
00:00:00.000 --> 00:01:86.000
This is just a short

2
00:01:86.000 --> 00:03:18.000
demo of the transcription service

The transcription service can transcribe files with different languages in them (though it cannot distinguish between more than one language per file). Telling the service which language the media file you wish to have transcribed predominantly features will likely improve the quality of that transcription.

By default, all media files are assumed to be British English, and will be transcribed as such.

To specify a language to be used in the transcription process, pass the language code with a query parameter when you make a request to the transcribe endpoint.

?token=[VALID_TOKEN]&languagecode=en-us

For a list of supported language codes, refer here.

The process for transcribing the content is as follows:

1. The passed media file is converted into a WAV file
2. If the converted wav file is longer than 30 seconds, it is divided into 30 second chunks.
3. Each 30 second chunk is transcribed with the Google Speech API
4. When all transcriptions have completed, they are concatenated to form a transcript of the original WAV file in its entirety.
5. The original WAV file is then split according at each point of silence (< -20DBs) detected in the audio that is more than 0.2s long.
   • if no silences are detected, the WAV file is divided into equal 4 second long chunks
   • If the time between silences (a clip) is longer than 8 seconds, the clip is divided into 4 second chunks
6. These individual chunks are then transcribed with the same settings that were used to transribe the 30 second chunks earlier in the process. Each chunk has the combined transcript of the original WAV file passed along with it as a 'phrase' to help the Google Speech API determine what each chunk is supposed to say.
7. On completion of the transcriptions for each chunk, the data is returned to the client.