Upload Audio

Analyze emotions and speech patterns from audio content.

Submit audio from multiple sources and receive detailed emotion analysis, speaker identification, and transcription with emotional insights.

Input Sources:

• YouTube: Provide any YouTube video URL (audio will be extracted)
• Instagram: Instagram Reels and video posts (public content)
• Twitter/X: Twitter video posts and media content
• Facebook: Facebook video posts (public content)
• Cloud Storage: Dropbox, Google Drive, or other cloud service links
• Direct URL: Any publicly accessible audio or video link
• File Upload: Upload audio files directly from your device

Audio Extraction: Video files are supported for URLs only (YouTube, Instagram, Twitter, Facebook, Dropbox, direct links) - the audio track will be automatically extracted and analyzed. For file uploads, use audio formats only (MP3, WAV, AAC, M4A).

Analyze Specific Segments: Focus on specific portions using start_millis and end_millis parameters. This helps target key moments and optimize credit usage.

How It Works:

1. Submit your audio and receive an immediate response with an audio ID
2. Audio is queued for processing (typically completes within minutes)
3. Optionally receive a webhook notification via callback_url when complete
4. Retrieve results using the audio ID

Supported Formats:

• File Upload: MP3, WAV, AAC, M4A
• Media URLs: MP3, WAV, AAC, M4A, MP4, AVI, MOV (audio extracted from video URLs)

Credit Usage: Processing is billed at a base rate of 0.5 credits per second of analyzed audio duration (Professional and Enterprise plans: 0.25 credits per second). This base rate is discounted when speech-analysis stages are disabled — turning off transcription, diarization, and/or text_emotion lowers the per-second rate (skipping all three halves the speech cost). The discounted rate is applied before the credit is computed on the floored duration (rounded to whole seconds).

Extra Parameters:

• diarization (bool): Enable or disable speaker diarization for identifying different speakers in the audio. Default is True. Disabling it reduces the per-second credit cost.
• text_emotion (bool): Enable or disable transcript emotion analysis for the transcribed text. Default is True. Requires transcription=True; if transcription is False this is forced off. Disabling it reduces the per-second credit cost. Supplying a transcript is the exception: the words already exist, so text_emotion is honoured as you sent it.
• transcription (bool): Enable or disable transcript / ASR generation. Default is True. When False, no transcript is produced and text_emotion is forced off. Disabling it reduces the per-second credit cost.
• language (str, optional): Language code for the audio (e.g., 'en'). If not provided, language will be auto-detected.

Bringing your own transcript: Pass transcript (and optionally transcript_format) to skip our speech-to-text entirely. Your speaker turns become the diarization and your speaker names are used verbatim, so the analysis comes back attributed to real participants rather than "Speaker 1" / "Speaker 2" — and you are not billed for the transcription stage. A start_millis/end_millis trim is rejected: we would not have media your timestamps line up with. A media_url source is accepted — the download runs first and your transcript is applied once processing completes; that response's status is queue. After that apply we deliver callback_url (success or failure). A file or presigned upload applies in the same request and reports processing. The response reports speaker_count so you can see how many speakers we read out of what you sent.