{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ea7427c1", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2025-12-02T17:48:45.919628Z", "iopub.status.busy": "2025-12-02T17:48:45.919369Z", "iopub.status.idle": "2025-12-02T17:49:48.711164Z", "shell.execute_reply": "2025-12-02T17:49:48.710450Z" }, "papermill": { "duration": 62.796333, "end_time": "2025-12-02T17:49:48.712556", "exception": false, "start_time": "2025-12-02T17:48:45.916223", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m104.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m80.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m56.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m80.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n", "libcugraph-cu12 25.6.0 requires libraft-cu12==25.6.*, but you have libraft-cu12 25.2.0 which is incompatible.\r\n", "pylibcugraph-cu12 25.6.0 requires pylibraft-cu12==25.6.*, but you have pylibraft-cu12 25.2.0 which is incompatible.\r\n", "pylibcugraph-cu12 25.6.0 requires rmm-cu12==25.6.*, but you have rmm-cu12 25.2.0 which is incompatible.\u001b[0m\u001b[31m\r\n", "\u001b[0m" ] } ], "source": [ "# Cell 1: Install modern libraries compatible with Svara\n", "!pip install --quiet \"torch>=2.1.0\" \"transformers>=4.40.0\" \"peft>=0.10.0\" \"accelerate>=0.29.0\" \"snac\" \"soundfile\" \"pandas\" \"tqdm\"" ] }, { "cell_type": "code", "execution_count": 2, "id": "aac3ef61", "metadata": { "execution": { "iopub.execute_input": "2025-12-02T17:49:48.742991Z", "iopub.status.busy": "2025-12-02T17:49:48.742322Z", "iopub.status.idle": "2025-12-02T17:49:48.747929Z", "shell.execute_reply": "2025-12-02T17:49:48.747203Z" }, "papermill": { "duration": 0.021734, "end_time": "2025-12-02T17:49:48.749017", "exception": false, "start_time": "2025-12-02T17:49:48.727283", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adapter Path: /kaggle/input/svara-adapter-for-bhojpuri/bhj\n", "Input JSON: /kaggle/input/bhojpuri-nhc/BHOJPURI_NHC_FIRST_100_Transcripts.json\n", "Voice: Bhojpuri (Female)\n" ] } ], "source": [ "# Cell 2: Paths and Config\n", "import os\n", "\n", "# 1. Path to your Adapter\n", "# (If Kaggle unzipped it, point to the folder. If it's a zip, point to the zip file)\n", "# Example: \"/kaggle/input/my-adapter-dataset/svara_lora_adapter\"\n", "ADAPTER_PATH = \"/kaggle/input/svara-adapter-for-bhojpuri/bhj\"\n", "\n", "# 2. Path to your new JSON file\n", "INPUT_JSON_PATH = \"/kaggle/input/bhojpuri-nhc/BHOJPURI_NHC_FIRST_100_Transcripts.json\"\n", "\n", "# 3. Output settings\n", "OUTPUT_DIR = \"/kaggle/working/generated_audio\"\n", "OUTPUT_ZIP_NAME = \"svara_inference_results\"\n", "\n", "# Model settings\n", "MODEL_ID = \"kenpath/svara-tts-v1\"\n", "TARGET_SR = 24000\n", "DEVICE = \"cuda\"\n", "\n", "# Prompt Settings (Based on your JSON metadata)\n", "LANGUAGE = \"Bhojpuri\"\n", "GENDER = \"Female\" \n", "\n", "print(f\"Adapter Path: {ADAPTER_PATH}\")\n", "print(f\"Input JSON: {INPUT_JSON_PATH}\")\n", "print(f\"Voice: {LANGUAGE} ({GENDER})\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "f2661839", "metadata": { "execution": { "iopub.execute_input": "2025-12-02T17:49:48.778546Z", "iopub.status.busy": "2025-12-02T17:49:48.778336Z", "iopub.status.idle": "2025-12-02T17:51:37.032512Z", "shell.execute_reply": "2025-12-02T17:51:37.031622Z" }, "papermill": { "duration": 108.270798, "end_time": "2025-12-02T17:51:37.034105", "exception": false, "start_time": "2025-12-02T17:49:48.763307", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2025-12-02 17:50:03.010681: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1764697803.168153 20 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1764697803.214274 20 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "ename": "AttributeError", "evalue": "'MessageFactory' object has no attribute 'GetPrototype'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mAttributeError\u001b[0m: 'MessageFactory' object has no attribute 'GetPrototype'" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loading base model...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "623d01449502413191d9580c28de2702", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/899 [00:00 {voice_str}: {text}<|eot_id|>\"\n", " prompt = \"\" + formatted_text + \"\"\n", " \n", " # Tokenize\n", " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n", " \n", " # Add Special Tokens\n", " start_token = torch.tensor([[128259]], dtype=torch.int64)\n", " end_tokens = torch.tensor([[128009, 128260, 128261, 128257]], dtype=torch.int64)\n", " modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1).to(DEVICE)\n", "\n", " # Generate\n", " generated_ids = model.generate(\n", " input_ids=modified_input_ids,\n", " max_new_tokens=1024,\n", " do_sample=True,\n", " temperature=0.7,\n", " top_p=0.95,\n", " eos_token_id=128258\n", " )\n", "\n", " # Decode SNAC\n", " AUDIO_CODE_BASE = 128266\n", " row = generated_ids[0]\n", " start_mask = (row == 128257).nonzero(as_tuple=True)[0]\n", " if len(start_mask) == 0: return None\n", " \n", " audio_tokens = row[start_mask[-1].item() + 1:]\n", " audio_tokens = audio_tokens[audio_tokens != 128258]\n", " audio_tokens = audio_tokens[audio_tokens >= AUDIO_CODE_BASE]\n", " \n", " snac_tokens = (audio_tokens - AUDIO_CODE_BASE).tolist()\n", " snac_tokens = snac_tokens[:(len(snac_tokens) // 7) * 7]\n", " \n", " if not snac_tokens: return None\n", "\n", " # Hierarchical Decoding\n", " codes = [[] for _ in range(3)]\n", " offsets = [i * 4096 for i in range(7)]\n", " \n", " for i in range(0, len(snac_tokens), 7):\n", " codes[0].append(snac_tokens[i] - offsets[0])\n", " codes[1].extend([snac_tokens[i+1]-offsets[1], snac_tokens[i+4]-offsets[4]])\n", " codes[2].extend([snac_tokens[i+2]-offsets[2], snac_tokens[i+3]-offsets[3], \n", " snac_tokens[i+5]-offsets[5], snac_tokens[i+6]-offsets[6]])\n", " \n", " tensor_codes = [torch.tensor(c, dtype=torch.long, device=DEVICE).unsqueeze(0) for c in codes]\n", " audio = snac_model.decode(tensor_codes).squeeze().cpu().numpy()\n", " return audio" ] }, { "cell_type": "code", "execution_count": 5, "id": "ef4b6600", "metadata": { "execution": { "iopub.execute_input": "2025-12-02T17:51:37.132659Z", "iopub.status.busy": "2025-12-02T17:51:37.132407Z", "iopub.status.idle": "2025-12-02T18:33:57.731465Z", "shell.execute_reply": "2025-12-02T18:33:57.730723Z" }, "papermill": { "duration": 2540.617975, "end_time": "2025-12-02T18:33:57.732730", "exception": false, "start_time": "2025-12-02T17:51:37.114755", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parsing JSON...\n", "Loaded 100 sentences.\n", "Starting generation for 100 files...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dc52e2ee41134a1cb67f3c2ac268aa1e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating: 0%| | 0/100 [00:00 0:\n", "# print(\"Zipping files...\")\n", "# shutil.make_archive(OUTPUT_ZIP_NAME, 'zip', OUTPUT_DIR)\n", "# print(f\"✅ Download {OUTPUT_ZIP_NAME}.zip from Output!\")\n", "# else:\n", "# print(\"❌ No files were generated to zip.\")\n", "\n", "\n", "# Cell 5: Main Execution (with timing)\n", "import shutil\n", "import time\n", "import json\n", "import csv\n", "import numpy as np\n", "from statistics import median\n", "\n", "if os.path.exists(OUTPUT_DIR):\n", " shutil.rmtree(OUTPUT_DIR)\n", "os.makedirs(OUTPUT_DIR, exist_ok=True)\n", "\n", "# 1. Parse Input\n", "print(\"Parsing JSON...\")\n", "try:\n", " texts = parse_custom_json(INPUT_JSON_PATH)\n", " total_files = len(texts)\n", " print(f\"Loaded {total_files} sentences.\")\n", "except Exception as e:\n", " print(f\"Failed to load JSON: {e}\")\n", " texts = {}\n", " total_files = 0\n", "\n", "# 2. Generate with timing\n", "success_count = 0\n", "failure_count = 0\n", "timings = [] # list of dicts: {file_id, gen_time, write_time, success, error}\n", "\n", "print(f\"Starting generation for {total_files} files...\")\n", "\n", "for i, (file_id, text) in enumerate(tqdm(list(texts.items()), desc=\"Generating\")):\n", " wav_path = os.path.join(OUTPUT_DIR, f\"{file_id}.wav\")\n", " entry = {\"file_id\": file_id, \"gen_time_s\": None, \"write_time_s\": None, \"success\": False, \"error\": None}\n", " try:\n", " # measure generation (inference) time\n", " t0 = time.perf_counter()\n", " audio = generate_wav(text)\n", " t1 = time.perf_counter()\n", " gen_time = t1 - t0\n", " entry[\"gen_time_s\"] = gen_time\n", "\n", " if audio is not None:\n", " # measure disk write time\n", " t2 = time.perf_counter()\n", " sf.write(wav_path, audio, TARGET_SR)\n", " t3 = time.perf_counter()\n", " write_time = t3 - t2\n", " entry[\"write_time_s\"] = write_time\n", "\n", " success_count += 1\n", " entry[\"success\"] = True\n", " else:\n", " failure_count += 1\n", " entry[\"error\"] = \"No audio returned\"\n", " print(f\"[{i+1}/{total_files}] ⚠️ Skipped (No Audio): {file_id}\")\n", "\n", " except Exception as e:\n", " failure_count += 1\n", " entry[\"error\"] = repr(e)\n", " print(f\"[{i+1}/{total_files}] ❌ Error on {file_id}: {e}\")\n", "\n", " timings.append(entry)\n", "\n", "# 3. Zip (only zip if at least one success)\n", "print(f\"\\nFinished. Success: {success_count}/{total_files} Failures: {failure_count}\")\n", "\n", "if success_count > 0:\n", " print(\"Zipping files...\")\n", " shutil.make_archive(OUTPUT_ZIP_NAME, 'zip', OUTPUT_DIR)\n", " print(f\"✅ Download {OUTPUT_ZIP_NAME}.zip from Output!\")\n", "else:\n", " print(\"❌ No files were generated to zip.\")\n", "\n", "# 4. Save timings CSV and JSON summary\n", "csv_path = os.path.join(OUTPUT_DIR, \"inference_times.csv\")\n", "with open(csv_path, \"w\", newline=\"\", encoding=\"utf-8\") as f:\n", " writer = csv.DictWriter(f, fieldnames=[\"file_id\", \"gen_time_s\", \"write_time_s\", \"success\", \"error\"])\n", " writer.writeheader()\n", " for r in timings:\n", " writer.writerow(r)\n", "\n", "summary = {}\n", "gen_times = [r[\"gen_time_s\"] for r in timings if r[\"gen_time_s\"] is not None]\n", "write_times = [r[\"write_time_s\"] for r in timings if r[\"write_time_s\"] is not None]\n", "\n", "def stats(arr):\n", " if not arr:\n", " return {\"count\": 0}\n", " a = np.array(arr, dtype=float)\n", " return {\n", " \"count\": int(a.size),\n", " \"total_s\": float(a.sum()),\n", " \"mean_s\": float(a.mean()),\n", " \"median_s\": float(np.median(a)),\n", " \"min_s\": float(a.min()),\n", " \"max_s\": float(a.max()),\n", " \"p90_s\": float(np.percentile(a, 90)),\n", " \"p95_s\": float(np.percentile(a, 95)),\n", " }\n", "\n", "summary[\"generation\"] = stats(gen_times)\n", "summary[\"write\"] = stats(write_times)\n", "summary[\"counts\"] = {\"total\": total_files, \"success\": success_count, \"failure\": failure_count}\n", "summary_path = os.path.join(OUTPUT_DIR, \"inference_summary.json\")\n", "with open(summary_path, \"w\", encoding=\"utf-8\") as f:\n", " json.dump(summary, f, indent=2, ensure_ascii=False)\n", "\n", "# 5. Print readable timing summary\n", "print(\"\\n=== Inference timing summary ===\")\n", "if summary[\"generation\"].get(\"count\", 0) > 0:\n", " g = summary[\"generation\"]\n", " print(f\"Generation - n={g['count']}, total={g['total_s']:.2f}s, mean={g['mean_s']:.3f}s, median={g['median_s']:.3f}s, min={g['min_s']:.3f}s, max={g['max_s']:.3f}s, p90={g['p90_s']:.3f}s, p95={g['p95_s']:.3f}s\")\n", "else:\n", " print(\"No generation timings recorded.\")\n", "\n", "if summary[\"write\"].get(\"count\", 0) > 0:\n", " w = summary[\"write\"]\n", " print(f\"Disk write - n={w['count']}, total={w['total_s']:.2f}s, mean={w['mean_s']:.3f}s, median={w['median_s']:.3f}s, min={w['min_s']:.3f}s, max={w['max_s']:.3f}s\")\n", "else:\n", " print(\"No write timings recorded.\")\n", "\n", "print(\"\\nDetailed timings CSV saved to:\", csv_path)\n", "print(\"Summary JSON saved to:\", summary_path)\n" ] } ], "metadata": { "kaggle": { "accelerator": "nvidiaTeslaT4", "dataSources": [ { "datasetId": 8778149, 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