Removed Parakeet again as it requires PyTorch which is too large for this project

services/modules/transcription-local/parakeet.js

@@ -1,54 +0,0 @@
-// -----------------------------------------------------------
-//  Parakeet (Step 3A: spawn Python minimal integration)
-// -----------------------------------------------------------
-
-const fs = require("fs");
-const path = require("path");
-const { spawn } = require("child_process");
-
-module.exports = {
-    name: "parakeet",
-    type: "transcription",
-    displayname: "NVIDIA Parakeet",
-
-    async function(audioFilePath) {
-        console.log("🦜 [Parakeet] Starting test integration (spawn only)...");
-        console.log("🦜 Input audio:", audioFilePath);
-
-        // Check audio exists
-        if (!fs.existsSync(audioFilePath)) {
-            throw new Error("Audio file does not exist: " + audioFilePath);
-        }
-
-        // Output path in storage/transcripts
-        const sessionId = path.basename(audioFilePath).replace(/\.[^.]+$/, "");
-        const outputDir = path.join(__dirname, "../../../storage/transcripts");
-        fs.mkdirSync(outputDir, { recursive: true });
-
-        const outputPath = path.join(outputDir, `${sessionId}.json`);
-
-        // -------------------------------------------------------
-        // SPAWN PYTHON SCRIPT (step 3A — dummy script)
-        // -------------------------------------------------------
-        return new Promise((resolve, reject) => {
-            const python310 = "C:\\Users\\smith\\AppData\\Local\\Programs\\Python\\Python310\\python.exe";
-            const py = spawn(python310, [
-                path.join(__dirname, "parakeet_transcribe.py"),
-                audioFilePath,
-                outputPath
-            ]);
-
-            py.stdout.on("data", data => console.log("🦜 [Python]", data.toString().trim()));
-            py.stderr.on("data", data => console.error("🦜 [Python ERR]", data.toString().trim()));
-
-            py.on("close", code => {
-                if (code === 0) {
-                    console.log("🦜 [Parakeet] Done (spawn test). Output:", outputPath);
-                    resolve(outputPath);
-                } else {
-                    reject(new Error("Python script failed with exit code " + code));
-                }
-            });
-        });
-    }
-};

services/modules/transcription-local/parakeet_transcribe.py

@@ -1,71 +0,0 @@
-# -----------------------------------------------------------
-#  Parakeet Real Transcriber (NVIDIA NeMo + PyTorch GPU)
-# -----------------------------------------------------------
-
-import sys
-import json
-import soundfile as sf
-import torch
-from nemo.collections.asr.models import ASRModel
-
-# Args:
-#   sys.argv[1] = input audio path
-#   sys.argv[2] = output JSON path
-
-audio_path = sys.argv[1]
-output_path = sys.argv[2]
-
-print("🔥 Starting Parakeet model...")
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print("🔥 Using device:", device)
-
-# -----------------------------------------------------------
-# Load Parakeet model (NVIDIA pretrained ASR)
-# -----------------------------------------------------------
-model = ASRModel.from_pretrained(model_name="nvidia/parakeet-ctc-0.6b")
-model = model.to(device)
-model.eval()
-
-# -----------------------------------------------------------
-# Load audio
-# -----------------------------------------------------------
-print("🎧 Loading audio:", audio_path)
-audio, sr = sf.read(audio_path)
-
-# model expects mono float32
-if len(audio.shape) > 1:
-    audio = audio.mean(axis=1)
-
-audio = audio.astype("float32")
-
-# -----------------------------------------------------------
-# Run inference
-# -----------------------------------------------------------
-print("🧠 Running inference...")
-with torch.no_grad():
-    hyp = model.transcribe([audio])[0]
-
-# Extract only the text
-if hasattr(hyp, "text"):
-    transcript = hyp.text
-else:
-    # fallback: convert to string (rare)
-    transcript = str(hyp)
-
-print("📄 Transcript:", transcript)
-
-# -----------------------------------------------------------
-# Save JSON format compatible with V2D pipeline
-# -----------------------------------------------------------
-result = {
-    "id": output_path.split("/")[-1].replace(".json", ""),
-    "tool": "nemo_parakeet",
-    "status": "completed",
-    "text": transcript,
-    "words": []  # Parakeet XS doesn’t return word timestamps
-}
-
-with open(output_path, "w", encoding="utf-8") as f:
-    json.dump(result, f, indent=2, ensure_ascii=False)
-
-print("✔ JSON saved at:", output_path)