Extract speaker names from audio

To extract speaker names from audio, you typically need to perform the following steps using speaker diarization, speech recognition, and optionally speaker identification. Here’s how it’s done:

Step-by-Step Process to Extract Speaker Names from Audio

1. Speaker Diarization

• Goal: Split the audio into segments by speaker (e.g., Speaker 1, Speaker 2).

• Tools:

  • pyannote-audio – high accuracy for diarization.

  • Google Cloud Speech-to-Text (with diarization option).

  • AWS Transcribe or Azure Speech.

2. Automatic Speech Recognition (ASR)

• Goal: Convert spoken words to text.

• Tools:

  • Whisper by OpenAI (open-source, accurate).

  • Google Speech-to-Text.

  • IBM Watson, Microsoft Azure Speech, or DeepSpeech.

3. (Optional) Speaker Identification

• Goal: Match speaker segments to known names (requires voice samples or training).

• Tools:

  • Custom ML models (e.g., embedding-based matching with voice samples).

  • Face recognition + voice sync (in video context).

  • Manual mapping based on known speech content.

4. Combine Results

• Sync diarization labels (e.g., “Speaker 1”) with transcriptions.

• If speaker names are known or can be inferred from the transcript, map them accordingly.

Example Workflow Using Open-Source Tools

Requirements:

bash
CopyEdit
pip install pyannote-audio openai-whisper

Pseudocode:

python
CopyEdit
from pyannote.audio import Pipeline
import whisper

# Load diarization pipeline
diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token="YOUR_HF_TOKEN")

# Run diarization
diarization = diarization_pipeline("your_audio_file.wav")

# Transcribe with Whisper
model = whisper.load_model("base")
transcription = model.transcribe("your_audio_file.wav", verbose=True)

# Combine diarization + transcription
for turn, _, speaker in diarization.itertracks(yield_label=True):
    print(f"{speaker}: {transcription['text'][turn.start:turn.end]}")

Advanced: Assign Real Names

If you know the speakers or have voice samples:

• Use voice embedding techniques to match unknown speakers to known samples.

• Tools: Resemblyzer, SpeechBrain, pyannote.audio.

Alternatives with Minimal Code

Google Cloud (Auto-Diarization + ASR)

bash
CopyEdit
gcloud ml speech recognize-long-running 
    --language-code=en-US 
    --enable-speaker-diarization 
    --diarization-speaker-count=2 
    --audio-uri=gs://your-bucket/audio.wav

Let me know if you want a ready-to-run script, use in batch processing, or integration with a web app.