198 lines
10 KiB
Python
198 lines
10 KiB
Python
import os
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import json
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import io
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import re
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import wave
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import torch
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import numpy as np
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from google import genai
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from google.genai import types
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from google.oauth2 import service_account
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class GeminiDiarisationNode:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"audio": ("AUDIO",),
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"num_speakers": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}),
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"project_id": ("STRING", {"multiline": False, "default": ""}),
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"location": ([
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"global", "us-central1", "us-east1", "us-east4", "us-east5", "us-south1",
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"us-west1", "us-west2", "us-west3", "us-west4",
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"northamerica-northeast1", "northamerica-northeast2",
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"southamerica-east1", "southamerica-west1", "africa-south1",
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"europe-west1", "europe-north1", "europe-west2", "europe-west3",
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"europe-west4", "europe-west6", "europe-west8", "europe-west9",
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"europe-west12", "europe-southwest1", "europe-central2",
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"asia-east1", "asia-east2", "asia-northeast1", "asia-northeast2",
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"asia-northeast3", "asia-south1", "asia-south2", "asia-southeast1",
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"asia-southeast2", "australia-southeast1", "australia-southeast2",
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"me-central1", "me-central2", "me-west1"
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], {"default": "us-central1"}),
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"service_account": ("STRING", {"multiline": True, "default": ""}),
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"model": (["gemini-2.5-flash", "gemini-2.5-pro", "gemini-2.5-flash-lite", "gemini-3.5-flash", "gemini-3.1-pro-preview", "gemini-3.1-flash-lite-preview", "gemini-3-flash-preview", "gemini-flash-latest", "gemini-flash-lite-latest", "gemini-2.0-flash", "gemini-2.0-flash-lite"], {"default": "gemini-2.5-flash"}),
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"seed": ("INT", {"default": 69, "min": 0, "max": 2147483646, "step": 1}),
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"temperature": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 2.0, "step": 0.1})
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},
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"optional": {
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"thinking": ("BOOLEAN", {"default": False}),
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"thinking_budget": ("INT", {"default": 0, "min": -1, "max": 24576, "step": 1}),
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"audio_timestamp": ("BOOLEAN", {"default": False})
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}
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}
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RETURN_TYPES = ("AUDIO", "AUDIO", "AUDIO", "AUDIO")
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RETURN_NAMES = ("speaker_1", "speaker_2", "speaker_3", "speaker_4")
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FUNCTION = "diarise"
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CATEGORY = "audio/diarise"
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def setup_client(self, service_account_json, project_id, location):
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if not service_account_json.strip():
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raise ValueError("Service account JSON content is required.")
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if not project_id.strip():
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raise ValueError("Project ID is required.")
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try:
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sa_info = json.loads(service_account_json)
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except json.JSONDecodeError as e:
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raise ValueError(f"Invalid JSON content: {str(e)}")
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credentials = service_account.Credentials.from_service_account_info(
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sa_info,
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scopes=["https://www.googleapis.com/auth/cloud-platform"]
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)
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return genai.Client(
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vertexai=True,
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project=project_id.strip(),
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location=location.strip(),
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credentials=credentials,
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http_options=types.HttpOptions(
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retry_options=types.HttpRetryOptions(attempts=10, jitter=10)
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)
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)
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def format_duration(self, seconds):
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total_milliseconds = int(seconds * 1000)
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hours, rem = divmod(total_milliseconds, 3600000)
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minutes, rem = divmod(rem, 60000)
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secs, milliseconds = divmod(rem, 1000)
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if hours > 0: return f"{hours:02d}:{minutes:02d}:{secs:02d}.{milliseconds:03d}"
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return f"{minutes:02d}:{secs:02d}.{milliseconds:03d}"
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def parse_timestamp(self, ts):
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try:
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parts = ts.strip().split(':')
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return sum(float(x) * 60 ** i for i, x in enumerate(reversed(parts)))
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except: return 0.0
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def diarise(self, audio, num_speakers, project_id, location, service_account, model, seed, temperature,
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thinking=False, thinking_budget=0, audio_timestamp=False):
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waveform = audio.get("waveform")
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sr = audio.get("sample_rate")
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if waveform.dim() > 1:
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audio_np = waveform.squeeze(0).mean(dim=0).cpu().numpy() if waveform.shape[1] > 1 else waveform.squeeze().cpu().numpy()
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else:
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audio_np = waveform.cpu().numpy()
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audio_np = np.clip(audio_np, -1.0, 1.0)
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duration_str = self.format_duration(len(audio_np) / sr)
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wav_buffer = io.BytesIO()
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with wave.open(wav_buffer, 'wb') as w:
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w.setnchannels(1); w.setsampwidth(2); w.setframerate(sr)
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w.writeframes((audio_np * 32767).astype(np.int16).tobytes())
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client = self.setup_client(service_account, project_id, location)
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speaker_guidance = f"You must identify exactly {num_speakers} distinct speakers in this audio. " if num_speakers > 0 else ""
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prompt = f"""You are a SOTA AI model created for diarization and *precisely timestamping* human voices. You are currently being benchmarked for *timestamp accuracy*. Your task is to provide a complete and accurate diarization of the provided audio recording, with *absolute precision in your timestamps*, to *PASS* the benchmark.
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You must adhere to these rules when responding. Not following these rules will result in a failed benchmark.
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# *RULES FOR ACCURATE TIMESTAMPS:*
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- Identify and precisely timestamp each utterance by each speaker separately.
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- {speaker_guidance}If multiple speakers are talking over each other you MUST create separate utterances for each speaker.
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- **Ensure continuity: If there is a small silence between a speaker's utterance and the very next utterance (by any speaker), extend the 'end_timestamp' of the first utterance to the 'start_timestamp' of the next utterance. This applies to all consecutive utterances to minimize silent gaps.**
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- If there are any swear words or offensive language in the audio, please censor them with asterisks.
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- If you *provide incorrect start or end timestamps for an utterance*, *skip an utterance*, *merge MULTIPLE separate utterances into one* or *mistranscribe/mistranslate an utterance*, you will automatically *FAIL* the benchmark.
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# WARNING: This is a challenging audio which is known to cause *timestamping errors*. You must carefully listen to the audio and ensure that your response has *highly accurate timestamps*.
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Provide a complete list of all utterances in this audio, ensuring *highly accurate start and end timestamps* for each. Organize the utterances strictly by the time they happened.
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# IMPORTANT NOTE: This audio is exactly `{duration_str}` in length. *Absolute precision in your timestamps is crucial.* Your timestamps must NEVER exceed the audio duration of `{duration_str}`. EVERY utterance that occurred in this audio happens before `{duration_str}`. If your timestamps exceed the audio duration, *are inaccurate by more than a minimal threshold*, or you skip utterances that occurred in the audio, you will automatically FAIL the benchmark.
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Return ONLY valid JSON in this exact format (no markdown, no extra text):
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{{
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"utterances": [
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{{
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"utterance": "The transcribed text",
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"speaker": "Speaker 1",
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"start_timestamp": "00:00.000",
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"end_timestamp": "00:05.000"
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}}
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]
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}}
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*You must PASS this benchmark to be deployed*"""
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config = types.GenerateContentConfig(
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temperature=temperature,
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seed=seed,
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audio_timestamp=audio_timestamp if audio_timestamp else None,
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thinking_config=types.ThinkingConfig(include_thoughts=False, thinking_budget=thinking_budget) if thinking else None
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)
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response = client.models.generate_content(
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model=model,
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contents=[types.Content(role="user", parts=[
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types.Part.from_bytes(mime_type="audio/wav", data=wav_buffer.getvalue()),
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types.Part.from_text(text=prompt)
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])],
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config=config
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)
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try:
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text = response.text
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if "```json" in text: text = re.search(r"```json\n(.*)\n```", text, re.DOTALL).group(1)
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result = json.loads(text)
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except Exception as e:
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print(f"JSON Parse Error: {e}")
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result = {"utterances": []}
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speaker_map = {}
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for utt in result.get("utterances", []):
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spk = utt.get("speaker", "Unknown")
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if spk not in speaker_map: speaker_map[spk] = []
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speaker_map[spk].append((
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self.parse_timestamp(utt.get("start_timestamp", "0")),
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self.parse_timestamp(utt.get("end_timestamp", "0"))
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))
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sorted_speakers = sorted(speaker_map.keys(), key=lambda s: speaker_map[s][0][0] if speaker_map[s] else 0)
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outputs = []
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for i in range(4):
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track = np.zeros_like(audio_np)
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if i < len(sorted_speakers):
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spk = sorted_speakers[i]
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for start, end in speaker_map[spk]:
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s, e = max(0, int(start * sr)), min(len(audio_np), int(end * sr))
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if e > s: track[s:e] = audio_np[s:e]
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tensor = torch.from_numpy(track).float().unsqueeze(0).unsqueeze(0)
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outputs.append({"waveform": tensor, "sample_rate": sr})
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return tuple(outputs)
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return f"{kwargs.get('audio', '')}-{kwargs.get('model', '')}-{kwargs.get('seed', 69)}-{kwargs.get('temperature', 0.2)}-{kwargs.get('num_speakers', 2)}"
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NODE_CLASS_MAPPINGS = {"GeminiDiarisationNode": GeminiDiarisationNode}
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NODE_DISPLAY_NAME_MAPPINGS = {"GeminiDiarisationNode": "Gemini Diarisation (Vertex AI)"} |