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Migrate to WhisperX for speaker diarization

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Implement a sliding window audio buffer and update the transcriber to
use WhisperX for transcription, alignment, and speaker identification.
Update the pipeline to handle and store speaker-attributed transcripts.

Additionally, update the LLM processor's reasoning parameter to
"enable_thinking".
This commit is contained in:
charles
2026-05-26 21:48:30 -07:00
parent d0fcdfab01
commit f4c98fb2b9
7 changed files with 135 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.python-version
+1
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@@ -0,0 +1 @@
3.12
main.py
+6
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@@ -0,0 +1,6 @@
def main():
print("Hello from dnd-helpers!")
if __name__ == "__main__":
main()
pyproject.toml
+7
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@@ -0,0 +1,7 @@
[project]
name = "dnd-helpers"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = []
requirements.txt
+1 -1
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@@ -1,5 +1,5 @@
# Core dependencies for D&D Helpers
faster-whisper
whisperx
sounddevice
pydantic
textual
src/llm/processor.py
+2 -2
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@@ -83,7 +83,7 @@ class LLMProcessor:
model=self.model,
messages=messages,
response_format=response_format,
extra_body={"include_reasoning": False},
extra_body={"enable_thinking": False},
)
return response.choices[0].message.content
except Exception as e:
@@ -125,7 +125,7 @@ class LLMProcessor:
model=self.model,
messages=messages,
response_format={"type": "json_object"},
extra_body={"include_reasoning": False},
extra_body={"enable_thinking": False},
)
logger.info("LLM Processor (Extract): Response received from backend.")
src/pipeline/orchestrator.py
+54 -13
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@@ -4,6 +4,8 @@ import os
from pathlib import Path
from typing import List, Optional
import numpy as np
from src.llm.models import ExtractionResult
from src.llm.processor import LLMProcessor
from src.stt.listener import AudioListener
@@ -18,6 +20,12 @@ logging.basicConfig(
logging.FileHandler("pipeline.log"),
],
)
# Suppress verbose logging from STT libraries to keep the TUI clean
logging.getLogger("whisper").setLevel(logging.WARNING)
logging.getLogger("faster_whisper").setLevel(logging.WARNING)
logging.getLogger("pyannote").setLevel(logging.WARNING)
logging.getLogger("whisperx").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
@@ -40,6 +48,13 @@ class PipelineOrchestrator:
self.history = [] # List of strings (transcripts)
self.history_max_words = 1000
# STT Sliding Window Buffer
self.audio_buffer = [] # List of audio chunks
self.buffer_max_seconds = 30
self.sample_rate = 16000
self.buffer_max_samples = self.buffer_max_seconds * self.sample_rate
self.last_processed_end_time = 0.0
async def stt_worker(self):
"""
Worker that handles STT: Audio -> Text.
@@ -50,12 +65,35 @@ class PipelineOrchestrator:
# Get audio chunk from listener
audio_chunk = await self.listener.get_chunk()
# Transcribe
text = self.transcriber.transcribe(audio_chunk)
# Maintain sliding window buffer
self.audio_buffer.append(audio_chunk)
current_buffer_samples = sum(len(c) for c in self.audio_buffer)
if text:
logger.info(f"Transcribed: {text}")
await self.transcript_queue.put(text)
if current_buffer_samples > self.buffer_max_samples:
# Remove oldest chunks until we are within the buffer limit
while (
sum(len(c) for c in self.audio_buffer) > self.buffer_max_samples
):
self.audio_buffer.pop(0)
# Concatenate buffer for transcription
full_audio = np.concatenate(self.audio_buffer)
# Transcribe (WhisperX now returns a list of (speaker, text, start, end))
results = self.transcriber.transcribe(full_audio)
# Filter for only new segments that start after the last processed segment
new_segments = [
res for res in results if res[2] >= self.last_processed_end_time
]
if new_segments:
for speaker, text, start, end in new_segments:
logger.info(f"Transcribed: [{speaker}] {text}")
await self.transcript_queue.put((speaker, text))
self.last_processed_end_time = max(
self.last_processed_end_time, end
)
except Exception as e:
logger.error(f"STT Worker error: {e}")
@@ -70,14 +108,16 @@ class PipelineOrchestrator:
logger.info("LLM Worker started.")
while self.is_running:
try:
# Get raw text from transcript queue
raw_text = await self.transcript_queue.get()
# Get raw text from transcript queue (now a tuple of (speaker, text))
speaker, raw_text = await self.transcript_queue.get()
logger.info(f"LLM Worker: Processing text: {raw_text}")
logger.info(f"LLM Worker: Processing text from {speaker}: {raw_text}")
# 1. Prepare Context (Conversation History)
# Maintain history and truncate to max words
self.history.append(raw_text)
# Store as "Speaker X: [text]"
entry = f"{speaker}: {raw_text}"
self.history.append(entry)
full_history_text = " ".join(self.history)
words = full_history_text.split()
if len(words) > self.history_max_words:
@@ -119,7 +159,7 @@ class PipelineOrchestrator:
def _get_wiki_context(self) -> str:
"""
Reads all files in the lore directory and returns them as a single context string.
Reads all files in the lore directory and returns them as a 저희 context string.
"""
from src.persistence.lore import DATA_LORE_DIR
@@ -151,11 +191,12 @@ class PipelineOrchestrator:
# Pass the proposal queue to the app.
app = ConfirmationApp(proposal_queue=self.proposal_queue)
await app.run_async()
# Once the TUI exits, stop the entire pipeline
self.stop()
except Exception as e:
logger.error(f"TUI Worker error: {e}")
self.stop()
except asyncio.CancelledError:
pass
async def run(self):
"""
@@ -188,6 +229,6 @@ class PipelineOrchestrator:
def stop(self):
"""
Stops the pipeline.
Stops.
"""
self.is_running = False
src/stt/transcriber.py
+64 -22
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@@ -1,6 +1,9 @@
import logging
import os
from faster_whisper import WhisperModel
import numpy as np
import whisperx
from whisperx.diarize import DiarizationPipeline
# Do not call basicConfig here, as it's called in the orchestrator
logger = logging.getLogger(__name__)
@@ -8,62 +11,101 @@ logger = logging.getLogger(__name__)
class Transcriber:
"""
Converts audio chunks (numpy arrays) into text using faster-whisper.
Converts audio chunks (numpy arrays) into text and identifies speakers using WhisperX.
"""
def __init__(self, model_size="base", device="cpu", compute_type="int8"):
def __init__(
self, model_size="base", device="cpu", compute_type="int8", language="en"
):
"""
Initializes the faster-whisper model.
Initializes the WhisperX model and diarization pipeline.
Args:
model_size (str): The size of the model to use (e.g., "tiny", "base", "small").
device (str): The device to run the model on ("cpu" or "cuda").
compute_type (str): The compute type to use (e.g., "int8", "float16").
language (str): The language code for alignment (e.g., "en").
"""
self.device = device
self.compute_type = compute_type
self.language = language
logger.info(
f"Loading faster-whisper model: {model_size} on {device} ({compute_type})..."
f"Loading WhisperX model: {model_size} on {device} ({compute_type})..."
)
try:
self.model = WhisperModel(
# Load transcription model
self.model = whisperx.load_model(
model_size, device=device, compute_type=compute_type
)
logger.info("Model loaded successfully.")
# Load alignment model (required for accurate speaker assignment)
# model_dir=None allows automatic model selection based on the language
self.align_model, self.align_metadata = whisperx.load_align_model(
device=device, model_dir=None, language_code=self.language
)
self.diarize_model = DiarizationPipeline()
logger.info("WhisperX and Diarization models loaded successfully.")
except Exception as e:
logger.error(f"Failed to load faster-whisper model: {e}")
logger.error(f"Failed to load WhisperX models: {e}")
raise
def transcribe(self, audio_chunk):
"""
Transcribes a single audio chunk.
Transcribes an audio chunk and performs speaker diarization.
Args:
audio_chunk (np.ndarray): The audio data as a numpy array.
Returns:
str: The transcribed text.
list: A list of tuples (speaker_id, text).
"""
if audio_chunk is None:
return ""
return []
try:
# faster-whisper expects audio in float32 and 1D array
audio_data = audio_chunk.astype("float32").flatten()
# WhisperX expects audio in float32 and 1D array
audio = audio_chunk.astype("float32").flatten()
# Transcribe the audio
segments, info = self.model.transcribe(audio_data, beam_size=5)
# 1. Perform transcription
# batch_size is set to 16 for efficiency; can be adjusted based on VRAM
result = self.model.transcribe(audio, batch_size=16)
# Combine segments into a single string
text = " ".join([segment.text.strip() for segment in segments])
# 2. Perform alignment
# Alignment is necessary for the assign_words_to_speakers step
result_a = whisperx.align(
result["segments"],
self.align_model,
self.align_metadata,
audio,
self.device,
)
return text.strip()
# 3. Perform diarization
diarize_segments = self.diarize_model(audio)
# 4. Align transcription segments with speakers
result_final = whisperx.assign_word_speakers(diarize_segments, result_a)
# Extract (speaker_id, text, start, end) tuples from the final result
output = []
for segment in result_final.get("segments", []):
speaker = segment.get("speaker", "Unknown")
text = segment.get("text", "").strip()
start = segment.get("start", 0.0)
end = segment.get("end", 0.0)
if text:
output.append((speaker, text, start, end))
return output
except Exception as e:
logger.error(f"Transcription error: {e}")
return ""
logger.error(f"Transcription/Diarization error: {e}")
return []
def close(self):
"""
Explicitly release model resources if necessary.
"""
# faster-whisper's WhisperModel doesn't have a standard close(),
# but we'll provide this for consistency.
pass