whisper-speaker-emotion 0.1.0

Emotion-aware speaker tone analysis using pitch and RMS from Whisper diarized VTT with timestamps

whisper-speaker-emotion

Emotion-aware speaker analysis using audio pitch and loudness aligned with Whisper diarized VTT transcripts.

What this package does

This package enriches Whisper-generated transcripts with acoustic emotion cues by analyzing how something was said, not just what was said.

Given:

• an audio file (.mp3, .wav)
• a Whisper-generated WEBVTT file with speaker diarization (.vtt)

It:

• aligns audio with transcript timestamps
• extracts weighted pitch (F0) and RMS (loudness)
• computes speaker-wise baselines
• measures deviation from each speaker’s norm
• returns a text-only emotion-enriched transcript

Purpose

Large Language Models (LLMs) are very good at understanding what was said, but they lack awareness of how it was said.

In real conversations, emotion is often conveyed through:

• voice tone
• pitch variation
• loudness
• sudden changes in speaking energy

Text-only transcripts lose this information. For example, the sentence “I’m fine” can sound calm, irritated, or angry depending on the speaker’s tone — but all of these look identical in text.

This package exists to add missing acoustic context back into the transcript so that LLMs can make more confident and accurate sentiment judgments.

Context Engineering for LLMs

Instead of asking an LLM to infer emotion from text alone, this approach enriches the input with:

• weighted pitch (F0)
• loudness (RMS)
• speaker-normalized deviations from baseline

By providing this additional context, LLMs can:

• distinguish low vs high speech
• identify emotionally intense moments
• detect worst emotional spikes in conversations
• make sentiment analysis more reliable and explainable

This is an example of context engineering — improving LLM outputs not by changing the model, but by improving the quality and richness of the input context.

Supported Inputs

• Audio: .mp3, .wav
• Transcript: .vtt (Whisper format with <v SpeakerX> tags and timestamps with HH:MM:SS.mmm --> HH:MM:SS.mmm format)

Installation

pip install whisper-speaker-emotion

Usage

from whisper_speaker_emotion import analyze_speaker_emotion

text_output = analyze_speaker_emotion(
    audio_path="call.mp3",
    vtt_path="call.vtt"
)

print(text_output)

Output

• Returned as a string
• No files are created or modified
• Includes speaker-wise average pitch and RMS values
• Includes per-utterance pitch and RMS measurements
• Includes deviation from each speaker’s baseline
• Output is human-readable and suitable for LLM analysis pipelines

Sample Input

Whisper-generated VTT

WEBVTT

00:00:00.000 --> 00:00:04.000
<v Speaker0> Hi, I just wanted to give some feedback about my cab ride this morning.

00:00:04.000 --> 00:00:07.000
<v Speaker1> Sure, I can help you with that. Could you tell me what happened?

00:00:07.000 --> 00:00:13.000
<v Speaker0> Yeah, the driver was polite, but the car wasn’t clean. It kind of smelled bad inside.

00:00:13.000 --> 00:00:17.000
<v Speaker1> Oh, I’m really sorry to hear that. I’ll make a note of it.

00:00:17.000 --> 00:00:22.000
<v Speaker0> Also, he took a much longer route even after I gave him the right location. That was really annoying.

00:00:22.000 --> 00:00:25.000
<v Speaker1> I understand. We’ll check the route and talk to the driver.

00:00:25.000 --> 00:00:31.000
<v Speaker0> Okay, thanks. I just wanted to make sure this doesn’t happen again.

00:00:31.000 --> 00:00:34.000
<v Speaker1> Thanks for letting us know. We’ll ensure better service next time.

Sample Tone Analysis Output

WEBVTT

Average Pitch:
Speaker0: 230.45 Hz
Speaker1: 190.78 Hz

Average RMS:
Speaker0: 0.068
Speaker1: 0.061

00:00:00.000 --> 00:00:04.000
<v Speaker0> Hi, I just wanted to give some feedback about my cab ride this morning.
Pitch: 236.22 Hz | RMS: 0.066
Pitch Difference with Average: +5.77 Hz
RMS Difference with Average: -0.002

00:00:04.000 --> 00:00:07.000
<v Speaker1> Sure, I can help you with that. Could you tell me what happened?
Pitch: 192.45 Hz | RMS: 0.062
Pitch Difference with Average: +1.67 Hz
RMS Difference with Average: +0.001

00:00:07.000 --> 00:00:13.000
<v Speaker0> Yeah, the driver was polite, but the car wasn’t clean. It kind of smelled bad inside.
Pitch: 225.89 Hz | RMS: 0.072
Pitch Difference with Average: -4.56 Hz
RMS Difference with Average: +0.004

00:00:13.000 --> 00:00:17.000
<v Speaker1> Oh, I’m really sorry to hear that. I’ll make a note of it.
Pitch: 188.74 Hz | RMS: 0.059
Pitch Difference with Average: -2.04 Hz
RMS Difference with Average: -0.002

00:00:17.000 --> 00:00:22.000
<v Speaker0> Also, he took a much longer route even after I gave him the right location. That was really annoying.
Pitch: 276.83 Hz | RMS: 0.081
Pitch Difference with Average: +46.38 Hz
RMS Difference with Average: +0.013

Roadmap

v0.1.0 (Current)

• CPU-only execution
• Designed for accuracy and correctness over speed
• Suitable for offline analysis and batch processing

v0.2.0 (Planned)

• GPU acceleration support
• Faster pitch and RMS computation for large-scale audio workloads
• Optimized performance for high-volume and real-time pipelines

The roadmap focuses on improving performance and scalability while keeping the package and output format stable.

License

MIT License