Sales Conversations Analysis ML Project
Budget: $250 – $750 USD
Robust Speech-to-Text Pipeline for Sales Agent Training
Goal: Build a robust speech-to-text pipeline using OpenAI Whisper technology (via Hugging Face) to transcribe Norwegian sales conversations, analyze them with NLP for tone, roles, and buying signals, and structure the data for machine learning. Ultimately, this will form the training foundation for an automated sales agent that can mimic a human salesperson as closely as possible. Let me know if any improvements are needed
Recommended model choice: Use NbAiLab/nb-whisper-large (Whisper Large fine-tuned for Norwegian) for the highest accuracy. This model is specifically trained on Norwegian data and outperforms smaller models. The medium model is faster, but Large provides better precision (especially for dialects and less clear speech).
Verbatim vs. non-verbatim transcription: Choose the verbatim version for more literal transcription. A verbatim model retains details like filler words and pauses, which are valuable for ML training to capture nuances. Non-verbatim is cleaner but loses details important for tone and context analysis.
Extra enhancements: Use diarization (speaker separation) via WhisperX, emotion detection for tone analysis, and CRM integration for automation. This makes the agent more human-like by learning from realistic conversations.
Step 1: Recording conversations and CRM integration
Use a CRM system (HubSpot, Salesforce, Pipedrive) to automatically record and store sales calls. Enable recording in Zoom/Meet or dedicated call center tools. Save in MP3/WAV and use CRM API for metadata.
Improvement: Add outcome tagging (positive/negative) already here for ML training.
Step 2: Preparing and splitting audio files
Split large audio files into chunks (e.g., 30 minutes) with FFmpeg. Normalize audio and reduce noise with librosa/noisereduce.
Improvement: Cleaner audio improves transcription and training data quality.
Step 3: Transcription with Hugging Face Whisper models
Install Hugging Face Transformers and download NbAiLab/nb-whisper-large. Use chunking and timestamps for precise output.
Improvement: Use WhisperX for diarization so seller and customer are separated. This is critical for role analysis.
Step 4: NLP analysis for tone, roles, and buying signals
Use diarization data to tag seller/customer. Run sentiment analysis and emotion detection. Extract buying signals with NER or regex.
Improvement: Prosody analysis (tone, speed, pauses) can provide deeper, more human-like insights.
Step 5: Structuring data for ML training
Store data in JSON with metadata, transcription, speaker, tone, emotion, and buying signal fields. Consider Parquet for large datasets.
Improvement: Add extra annotations (summaries, sales techniques) for richer context.
Step 6: Analyzing patterns – what works in sales calls?
Analyze which phrases and features correlate with success. Use sentiment trajectories and feature importance. Visualize results.
Improvement: Use LLMs (GPT-4/5) to generate insights in natural language. Can be applied in RLHF.
Step 7: Training and fine-tuning the automated sales agent
Use supervised fine-tuning on an LLM with dialogue data. Complement with RLHF to optimize response strategies. Simulate conversations and include human feedback.
Improvement: Apply insights from Step 6 to shape training goals and generate synthetic conversations to strengthen the dataset.
Goal: Build a robust speech-to-text pipeline using OpenAI Whisper technology (via Hugging Face) to transcribe Norwegian sales conversations, analyze them with NLP for tone, roles, and buying signals, and structure the data for machine learning. Ultimately, this will form the training foundation for an automated sales agent that can mimic a human salesperson as closely as possible. Let me know if any improvements are needed
Recommended model choice: Use NbAiLab/nb-whisper-large (Whisper Large fine-tuned for Norwegian) for the highest accuracy. This model is specifically trained on Norwegian data and outperforms smaller models. The medium model is faster, but Large provides better precision (especially for dialects and less clear speech).
Verbatim vs. non-verbatim transcription: Choose the verbatim version for more literal transcription. A verbatim model retains details like filler words and pauses, which are valuable for ML training to capture nuances. Non-verbatim is cleaner but loses details important for tone and context analysis.
Extra enhancements: Use diarization (speaker separation) via WhisperX, emotion detection for tone analysis, and CRM integration for automation. This makes the agent more human-like by learning from realistic conversations.
Step 1: Recording conversations and CRM integration
Use a CRM system (HubSpot, Salesforce, Pipedrive) to automatically record and store sales calls. Enable recording in Zoom/Meet or dedicated call center tools. Save in MP3/WAV and use CRM API for metadata.
Improvement: Add outcome tagging (positive/negative) already here for ML training.
Step 2: Preparing and splitting audio files
Split large audio files into chunks (e.g., 30 minutes) with FFmpeg. Normalize audio and reduce noise with librosa/noisereduce.
Improvement: Cleaner audio improves transcription and training data quality.
Step 3: Transcription with Hugging Face Whisper models
Install Hugging Face Transformers and download NbAiLab/nb-whisper-large. Use chunking and timestamps for precise output.
Improvement: Use WhisperX for diarization so seller and customer are separated. This is critical for role analysis.
Step 4: NLP analysis for tone, roles, and buying signals
Use diarization data to tag seller/customer. Run sentiment analysis and emotion detection. Extract buying signals with NER or regex.
Improvement: Prosody analysis (tone, speed, pauses) can provide deeper, more human-like insights.
Step 5: Structuring data for ML training
Store data in JSON with metadata, transcription, speaker, tone, emotion, and buying signal fields. Consider Parquet for large datasets.
Improvement: Add extra annotations (summaries, sales techniques) for richer context.
Step 6: Analyzing patterns – what works in sales calls?
Analyze which phrases and features correlate with success. Use sentiment trajectories and feature importance. Visualize results.
Improvement: Use LLMs (GPT-4/5) to generate insights in natural language. Can be applied in RLHF.
Step 7: Training and fine-tuning the automated sales agent
Use supervised fine-tuning on an LLM with dialogue data. Complement with RLHF to optimize response strategies. Simulate conversations and include human feedback.
Improvement: Apply insights from Step 6 to shape training goals and generate synthetic conversations to strengthen the dataset.