Sirshendu Ganguly
056ade0661
- Use librosa instead of torchaudio for mediaStorage - Created dedicated toggle handlers for Image Inference, Video Inference, Upscaler, Audio Inference, Accelerator Options, Bytedance Provider Settings, and OpenAI Provider Settings - Fixed Audio Inference widget ordering (use parameters now appear before their corresponding parameters) - All handlers use the same proven pattern from videoInferenceDimensionsHandler - Improved widget disabling/enabling functionality across all nodes
275 lines
10 KiB
Python
275 lines
10 KiB
Python
import json
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import uuid
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import requests
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import torch
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import numpy as np
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import librosa
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import tempfile
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import os
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from .utils import runwareUtils as rwUtils
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class RunwareAudioInference:
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"""Runware Audio Inference node for generating audio using Runware API"""
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def __init__(self):
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pass
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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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"model": ("RUNWAREAUDIOMODEL", {
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"tooltip": "AI model to use for audio generation"
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}),
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"usePositivePrompt": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable/disable positivePrompt parameter in API request"
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}),
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"positivePrompt": ("STRING", {
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"multiline": True,
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"default": "classical piano piece, gentle and melodic",
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"tooltip": "Text description that guides the audio generation process"
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}),
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"negativePrompt": ("STRING", {
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"multiline": True,
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"default": "",
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"tooltip": "Text description of what you don't want in the audio"
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}),
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"useDuration": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable/disable duration parameter in API request"
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}),
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"duration": ("INT", {
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"default": 10,
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"min": 10,
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"max": 300,
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"step": 1,
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"tooltip": "Length of generated audio in seconds (10-300)"
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}),
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"sampleRate": ("INT", {
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"default": 22050,
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"min": 8000,
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"max": 48000,
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"step": 1,
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"tooltip": "Sample rate of the generated audio in Hz (8000-48000)"
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}),
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"bitrate": ("INT", {
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"default": 32,
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"min": 32,
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"max": 320,
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"step": 32,
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"tooltip": "Bitrate of the generated audio in kbps (32-320)"
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}),
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"outputType": (["URL", "dataURI", "base64Data"], {
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"default": "URL",
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"tooltip": "Output type for the generated audio"
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}),
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"outputFormat": (["MP3"], {
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"default": "MP3",
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"tooltip": "Format of the output audio"
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}),
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"numberResults": ("INT", {
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"default": 1,
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"min": 1,
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"max": 3,
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"tooltip": "Number of audio files to generate"
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}),
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},
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"optional": {
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"providerSettings": ("RUNWAREPROVIDERSETTINGS", {
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"tooltip": "Provider-specific configuration settings"
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}),
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}
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio",)
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FUNCTION = "generateAudio"
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CATEGORY = "Runware/Audio"
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def generateAudio(self, **kwargs):
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"""Main function to generate audio using Runware API"""
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params = self._extractParameters(kwargs)
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genConfig = self._buildGenConfig(params)
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genResult = rwUtils.inferenecRequest(genConfig)
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self._logResponse(genResult)
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self._validateResponse(genResult)
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audioUrl = self._extractAudioUrl(genResult)
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audioObj = self._downloadAndProcessAudio(audioUrl, params["sampleRate"])
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return (audioObj,)
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def _extractParameters(self, kwargs):
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"""Extract and validate parameters from kwargs"""
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return {
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"positivePrompt": kwargs.get("positivePrompt", ""),
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"usePositivePrompt": kwargs.get("usePositivePrompt", True),
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"model": kwargs.get("model", ""),
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"duration": kwargs.get("duration", 30),
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"useDuration": kwargs.get("useDuration", True),
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"sampleRate": kwargs.get("sampleRate", 44100),
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"bitrate": kwargs.get("bitrate", 128),
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"outputType": kwargs.get("outputType", "URL"),
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"outputFormat": kwargs.get("outputFormat", "MP3"),
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"negativePrompt": kwargs.get("negativePrompt", ""),
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"numberResults": kwargs.get("numberResults", 1),
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"providerSettings": kwargs.get("providerSettings", None),
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}
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def _hasSections(self, providerSettings):
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"""Check if provider settings contain sections"""
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if not providerSettings or not isinstance(providerSettings, dict):
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return False
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elevenlabsSettings = providerSettings.get("elevenlabs", {})
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musicSettings = elevenlabsSettings.get("music", {})
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compositionPlan = musicSettings.get("compositionPlan", {})
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sections = compositionPlan.get("sections", [])
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return len(sections) > 0
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def _buildGenConfig(self, params):
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"""Build the generation configuration for API request"""
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taskUuid = str(uuid.uuid4())
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genConfig = [{
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"taskType": "audioInference",
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"taskUUID": taskUuid,
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"model": params["model"],
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"outputType": params["outputType"],
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"outputFormat": params["outputFormat"],
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"deliveryMethod": "sync",
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"includeCost": True,
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"numberResults": params["numberResults"],
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"audioSettings": {
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"sampleRate": params["sampleRate"],
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"bitrate": params["bitrate"]
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}
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}]
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# Handle sections - disable duration if sections are provided
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hasSections = self._hasSections(params["providerSettings"])
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if hasSections:
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params["useDuration"] = False
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print(f"[DEBUG] Disabled duration because sections are provided")
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# Add prompts conditionally
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if params["usePositivePrompt"]:
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genConfig[0]["positivePrompt"] = params["positivePrompt"]
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print(f"[DEBUG] Added positivePrompt: '{params['positivePrompt']}'")
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else:
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print(f"[DEBUG] Skipped positivePrompt")
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if params["useDuration"]:
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genConfig[0]["duration"] = params["duration"]
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print(f"[DEBUG] Added duration: {params['duration']}")
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else:
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print(f"[DEBUG] Skipped duration")
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# Add optional parameters
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if params["negativePrompt"]:
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genConfig[0]["negativePrompt"] = params["negativePrompt"]
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if params["providerSettings"] is not None:
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genConfig[0]["providerSettings"] = params["providerSettings"]
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print(f"[DEBUG] Sending Audio Inference Request:")
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print(f"[DEBUG] Request Payload: {json.dumps(genConfig, indent=2)}")
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return genConfig
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def _logResponse(self, genResult):
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"""Log the API response for debugging"""
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print(f"[DEBUG] Received Audio Inference Response:")
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print(f"[DEBUG] Response: {json.dumps(genResult, indent=2)}")
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def _validateResponse(self, genResult):
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"""Validate API response and raise errors if needed"""
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if "errors" in genResult:
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errorMessage = genResult["errors"][0]["message"]
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raise Exception(f"Audio generation failed: {errorMessage}")
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if "data" not in genResult or len(genResult["data"]) == 0:
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raise Exception("No audio data received from API")
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def _extractAudioUrl(self, genResult):
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"""Extract audio URL from API response"""
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audioData = genResult["data"][0]
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audioUrl = audioData.get("audioURL", "")
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if not audioUrl:
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audioUrl = audioData.get("audioDataURI", "")
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if not audioUrl:
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audioUrl = audioData.get("audioBase64Data", "")
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if not audioUrl:
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raise Exception("No audio URL received from API")
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return audioUrl
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def _downloadAndProcessAudio(self, audioUrl, targetSampleRate):
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"""Download audio file and process it for ComfyUI"""
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try:
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response = requests.get(audioUrl, timeout=30)
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response.raise_for_status()
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with tempfile.NamedTemporaryFile(suffix='.mp3', delete=False) as tempFile:
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tempFile.write(response.content)
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tempFilePath = tempFile.name
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try:
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waveformNp, originalSampleRate = self._loadAudioFile(tempFilePath)
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waveformNp = self._resampleAudio(waveformNp, originalSampleRate, targetSampleRate)
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waveformTensor = self._convertToTensor(waveformNp)
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return {
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"waveform": waveformTensor,
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"sample_rate": targetSampleRate
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}
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finally:
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if os.path.exists(tempFilePath):
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os.unlink(tempFilePath)
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except Exception as e:
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raise Exception(f"Failed to download audio: {e}")
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def _loadAudioFile(self, filePath):
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"""Load audio file using librosa"""
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waveformNp, originalSampleRate = librosa.load(filePath, sr=None, mono=False)
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# Convert to [samples, channels] format
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if waveformNp.ndim == 1:
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waveformNp = waveformNp.reshape(-1, 1)
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else:
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waveformNp = waveformNp.T # [channels, samples] -> [samples, channels]
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return waveformNp, originalSampleRate
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def _resampleAudio(self, waveformNp, originalSampleRate, targetSampleRate):
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"""Resample audio to target sample rate"""
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if originalSampleRate == targetSampleRate:
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return waveformNp
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if waveformNp.ndim == 1:
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return librosa.resample(waveformNp, orig_sr=originalSampleRate, target_sr=targetSampleRate)
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else:
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# Multi-channel: resample each channel
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return librosa.resample(waveformNp.T, orig_sr=originalSampleRate, target_sr=targetSampleRate).T
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def _convertToTensor(self, waveformNp):
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"""Convert numpy array to PyTorch tensor with correct format"""
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# Ensure 2D shape [samples, channels]
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if waveformNp.ndim == 1:
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waveformNp = waveformNp.reshape(-1, 1)
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# Convert to [channels, samples] for ComfyUI
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waveformTensor = torch.from_numpy(waveformNp.T).float()
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# Add batch dimension if needed: [batch, channels, samples]
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if waveformTensor.dim() == 2:
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waveformTensor = waveformTensor.unsqueeze(0)
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return waveformTensor
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