avoid zero size batch caused by grad_accumulation

inference_tts.ipynb

@@ -32,6 +32,7 @@
     "import torchaudio\n",
     "import numpy as np\n",
     "import random\n",
+    "from argparse import Namespace\n",
     "\n",
     "from data.tokenizer import (\n",
     "    AudioTokenizer,\n",
@@ -45,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# install MFA models and dictionaries if you haven't done so already\n",
+    "# install MFA models and dictionaries if you haven't done so already, already done in the dockerfile or envrionment setup\n",
     "!source ~/.bashrc && \\\n",
     "    conda activate voicecraft && \\\n",
     "    mfa model download dictionary english_us_arpa && \\\n",
@@ -61,28 +62,38 @@
     "# load model, encodec, and phn2num\n",
     "# # load model, tokenizer, and other necessary files\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "voicecraft_name=\"giga330M.pth\" # or gigaHalfLibri330M_TTSEnhanced_max16s.pth, giga830M.pth\n",
+    "\n",
+    "# the old way of loading the model\n",
     "from models import voicecraft\n",
-    "#import models.voicecraft as voicecraft\n",
-    "voicecraft_name=\"gigaHalfLibri330M_TTSEnhanced_max16s.pth\" # or giga330M.pth, giga830M.pth\n",
     "ckpt_fn =f\"./pretrained_models/{voicecraft_name}\"\n",
-    "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
     "if not os.path.exists(ckpt_fn):\n",
     "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/{voicecraft_name}\\?download\\=true\")\n",
     "    os.system(f\"mv {voicecraft_name}\\?download\\=true ./pretrained_models/{voicecraft_name}\")\n",
-    "if not os.path.exists(encodec_fn):\n",
-    "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n",
-    "    os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n",
-    "\n",
     "ckpt = torch.load(ckpt_fn, map_location=\"cpu\")\n",
     "model = voicecraft.VoiceCraft(ckpt[\"config\"])\n",
     "model.load_state_dict(ckpt[\"model\"])\n",
+    "phn2num = ckpt['phn2num']\n",
+    "config = vars(ckpt['config'])\n",
     "model.to(device)\n",
     "model.eval()\n",
     "\n",
-    "phn2num = ckpt['phn2num']\n",
+    "# # the new way of loading the model, with huggingface, this doesn't work yet\n",
+    "# from models.voicecraft import VoiceCraftHF\n",
+    "# model = VoiceCraftHF.from_pretrained(f\"pyp1/VoiceCraft_{voicecraft_name.replace('.pth', '')}\")\n",
+    "# phn2num = model.args.phn2num # or model.args['phn2num']?\n",
+    "# config = model.config\n",
+    "# model.to(device)\n",
+    "# model.eval()\n",
     "\n",
-    "text_tokenizer = TextTokenizer(backend=\"espeak\")\n",
-    "audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) # will also put the neural codec model on gpu\n"
+    "\n",
+    "encodec_fn = \"./pretrained_models/encodec_4cb2048_giga.th\"\n",
+    "if not os.path.exists(encodec_fn):\n",
+    "    os.system(f\"wget https://huggingface.co/pyp1/VoiceCraft/resolve/main/encodec_4cb2048_giga.th\")\n",
+    "    os.system(f\"mv encodec_4cb2048_giga.th ./pretrained_models/encodec_4cb2048_giga.th\")\n",
+    "audio_tokenizer = AudioTokenizer(signature=encodec_fn, device=device) # will also put the neural codec model on gpu\n",
+    "\n",
+    "text_tokenizer = TextTokenizer(backend=\"espeak\")\n"
    ]
   },
   {
@@ -148,7 +159,7 @@
     "\n",
     "# NOTE adjust the below three arguments if the generation is not as good\n",
     "stop_repetition = 3 # NOTE if the model generate long silence, reduce the stop_repetition to 3, 2 or even 1\n",
-    "sample_batch_size = 2 # for gigaHalfLibri330M_TTSEnhanced_max16s.pth, 1 or 2 should be fine since the model is trained to do TTS, for the other two models, might need a higher number. NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
+    "sample_batch_size = 4 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
     "seed = 1 # change seed if you are still unhappy with the result\n",
     "\n",
     "def seed_everything(seed):\n",
@@ -163,7 +174,7 @@
     "\n",
     "decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache, \"codec_audio_sr\": codec_audio_sr, \"codec_sr\": codec_sr, \"silence_tokens\": silence_tokens, \"sample_batch_size\": sample_batch_size}\n",
     "from inference_tts_scale import inference_one_sample\n",
-    "concated_audio, gen_audio = inference_one_sample(model, ckpt[\"config\"], phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n",
+    "concated_audio, gen_audio = inference_one_sample(model, Namespace(**config), phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n",
     "        \n",
     "# save segments for comparison\n",
     "concated_audio, gen_audio = concated_audio[0].cpu(), gen_audio[0].cpu()\n",
@@ -190,6 +201,13 @@
     "\n",
     "# you are might get warnings like WARNING:phonemizer:words count mismatch on 300.0% of the lines (3/1), this can be safely ignored"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

models/voicecraft.py

@@ -462,6 +462,8 @@ class VoiceCraft(nn.Module):
             before padding.
         """
         x, x_lens, y, y_lens = batch["x"], batch["x_lens"], batch["y"], batch["y_lens"]
+        if len(x) == 0:
+            return None
         x = x[:, :x_lens.max()] # this deal with gradient accumulation, where x_lens.max() might not be longer than the length of the current slice of x
         y = y[:, :, :y_lens.max()]
         assert x.ndim == 2, x.shape

steps/trainer.py

@@ -90,6 +90,8 @@ class Trainer:
                     cur_batch = {key: batch[key][cur_ind] for key in batch}
                     with torch.cuda.amp.autocast(dtype=torch.float16 if self.args.precision=="float16" else torch.float32):
                         out = self.model(cur_batch)
+                        if out == None:
+                            continue
 
                     record_loss = out['loss'].detach().to(self.rank) 
                     top10acc = out['top10acc'].to(self.rank)