{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hewliyang/speech-to-speech-translation/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import torch\n",
"import librosa\n",
"from transformers import VitsModel, VitsTokenizer, pipeline\n",
"from IPython.display import Audio"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cuda:0 with dtype torch.float16\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at facebook/mms-tts-zlm were not used when initializing VitsModel: ['flow.flows.0.wavenet.in_layers.0.weight_g', 'flow.flows.0.wavenet.in_layers.0.weight_v', 'flow.flows.0.wavenet.in_layers.1.weight_g', 'flow.flows.0.wavenet.in_layers.1.weight_v', 'flow.flows.0.wavenet.in_layers.2.weight_g', 'flow.flows.0.wavenet.in_layers.2.weight_v', 'flow.flows.0.wavenet.in_layers.3.weight_g', 'flow.flows.0.wavenet.in_layers.3.weight_v', 'flow.flows.0.wavenet.res_skip_layers.0.weight_g', 'flow.flows.0.wavenet.res_skip_layers.0.weight_v', 'flow.flows.0.wavenet.res_skip_layers.1.weight_g', 'flow.flows.0.wavenet.res_skip_layers.1.weight_v', 'flow.flows.0.wavenet.res_skip_layers.2.weight_g', 'flow.flows.0.wavenet.res_skip_layers.2.weight_v', 'flow.flows.0.wavenet.res_skip_layers.3.weight_g', 'flow.flows.0.wavenet.res_skip_layers.3.weight_v', 'flow.flows.1.wavenet.in_layers.0.weight_g', 'flow.flows.1.wavenet.in_layers.0.weight_v', 'flow.flows.1.wavenet.in_layers.1.weight_g', 'flow.flows.1.wavenet.in_layers.1.weight_v', 'flow.flows.1.wavenet.in_layers.2.weight_g', 'flow.flows.1.wavenet.in_layers.2.weight_v', 'flow.flows.1.wavenet.in_layers.3.weight_g', 'flow.flows.1.wavenet.in_layers.3.weight_v', 'flow.flows.1.wavenet.res_skip_layers.0.weight_g', 'flow.flows.1.wavenet.res_skip_layers.0.weight_v', 'flow.flows.1.wavenet.res_skip_layers.1.weight_g', 'flow.flows.1.wavenet.res_skip_layers.1.weight_v', 'flow.flows.1.wavenet.res_skip_layers.2.weight_g', 'flow.flows.1.wavenet.res_skip_layers.2.weight_v', 'flow.flows.1.wavenet.res_skip_layers.3.weight_g', 'flow.flows.1.wavenet.res_skip_layers.3.weight_v', 'flow.flows.2.wavenet.in_layers.0.weight_g', 'flow.flows.2.wavenet.in_layers.0.weight_v', 'flow.flows.2.wavenet.in_layers.1.weight_g', 'flow.flows.2.wavenet.in_layers.1.weight_v', 'flow.flows.2.wavenet.in_layers.2.weight_g', 'flow.flows.2.wavenet.in_layers.2.weight_v', 'flow.flows.2.wavenet.in_layers.3.weight_g', 'flow.flows.2.wavenet.in_layers.3.weight_v', 'flow.flows.2.wavenet.res_skip_layers.0.weight_g', 'flow.flows.2.wavenet.res_skip_layers.0.weight_v', 'flow.flows.2.wavenet.res_skip_layers.1.weight_g', 'flow.flows.2.wavenet.res_skip_layers.1.weight_v', 'flow.flows.2.wavenet.res_skip_layers.2.weight_g', 'flow.flows.2.wavenet.res_skip_layers.2.weight_v', 'flow.flows.2.wavenet.res_skip_layers.3.weight_g', 'flow.flows.2.wavenet.res_skip_layers.3.weight_v', 'flow.flows.3.wavenet.in_layers.0.weight_g', 'flow.flows.3.wavenet.in_layers.0.weight_v', 'flow.flows.3.wavenet.in_layers.1.weight_g', 'flow.flows.3.wavenet.in_layers.1.weight_v', 'flow.flows.3.wavenet.in_layers.2.weight_g', 'flow.flows.3.wavenet.in_layers.2.weight_v', 'flow.flows.3.wavenet.in_layers.3.weight_g', 'flow.flows.3.wavenet.in_layers.3.weight_v', 'flow.flows.3.wavenet.res_skip_layers.0.weight_g', 'flow.flows.3.wavenet.res_skip_layers.0.weight_v', 'flow.flows.3.wavenet.res_skip_layers.1.weight_g', 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'posterior_encoder.wavenet.in_layers.15.weight_g', 'posterior_encoder.wavenet.in_layers.15.weight_v', 'posterior_encoder.wavenet.in_layers.2.weight_g', 'posterior_encoder.wavenet.in_layers.2.weight_v', 'posterior_encoder.wavenet.in_layers.3.weight_g', 'posterior_encoder.wavenet.in_layers.3.weight_v', 'posterior_encoder.wavenet.in_layers.4.weight_g', 'posterior_encoder.wavenet.in_layers.4.weight_v', 'posterior_encoder.wavenet.in_layers.5.weight_g', 'posterior_encoder.wavenet.in_layers.5.weight_v', 'posterior_encoder.wavenet.in_layers.6.weight_g', 'posterior_encoder.wavenet.in_layers.6.weight_v', 'posterior_encoder.wavenet.in_layers.7.weight_g', 'posterior_encoder.wavenet.in_layers.7.weight_v', 'posterior_encoder.wavenet.in_layers.8.weight_g', 'posterior_encoder.wavenet.in_layers.8.weight_v', 'posterior_encoder.wavenet.in_layers.9.weight_g', 'posterior_encoder.wavenet.in_layers.9.weight_v', 'posterior_encoder.wavenet.res_skip_layers.0.weight_g', 'posterior_encoder.wavenet.res_skip_layers.0.weight_v', 'posterior_encoder.wavenet.res_skip_layers.1.weight_g', 'posterior_encoder.wavenet.res_skip_layers.1.weight_v', 'posterior_encoder.wavenet.res_skip_layers.10.weight_g', 'posterior_encoder.wavenet.res_skip_layers.10.weight_v', 'posterior_encoder.wavenet.res_skip_layers.11.weight_g', 'posterior_encoder.wavenet.res_skip_layers.11.weight_v', 'posterior_encoder.wavenet.res_skip_layers.12.weight_g', 'posterior_encoder.wavenet.res_skip_layers.12.weight_v', 'posterior_encoder.wavenet.res_skip_layers.13.weight_g', 'posterior_encoder.wavenet.res_skip_layers.13.weight_v', 'posterior_encoder.wavenet.res_skip_layers.14.weight_g', 'posterior_encoder.wavenet.res_skip_layers.14.weight_v', 'posterior_encoder.wavenet.res_skip_layers.15.weight_g', 'posterior_encoder.wavenet.res_skip_layers.15.weight_v', 'posterior_encoder.wavenet.res_skip_layers.2.weight_g', 'posterior_encoder.wavenet.res_skip_layers.2.weight_v', 'posterior_encoder.wavenet.res_skip_layers.3.weight_g', 'posterior_encoder.wavenet.res_skip_layers.3.weight_v', 'posterior_encoder.wavenet.res_skip_layers.4.weight_g', 'posterior_encoder.wavenet.res_skip_layers.4.weight_v', 'posterior_encoder.wavenet.res_skip_layers.5.weight_g', 'posterior_encoder.wavenet.res_skip_layers.5.weight_v', 'posterior_encoder.wavenet.res_skip_layers.6.weight_g', 'posterior_encoder.wavenet.res_skip_layers.6.weight_v', 'posterior_encoder.wavenet.res_skip_layers.7.weight_g', 'posterior_encoder.wavenet.res_skip_layers.7.weight_v', 'posterior_encoder.wavenet.res_skip_layers.8.weight_g', 'posterior_encoder.wavenet.res_skip_layers.8.weight_v', 'posterior_encoder.wavenet.res_skip_layers.9.weight_g', 'posterior_encoder.wavenet.res_skip_layers.9.weight_v']\n",
"- This IS expected if you are initializing VitsModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing VitsModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of VitsModel were not initialized from the model checkpoint at facebook/mms-tts-zlm and are newly initialized: ['flow.flows.0.wavenet.in_layers.0.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.0.parametrizations.weight.original1', 'flow.flows.0.wavenet.in_layers.1.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.1.parametrizations.weight.original1', 'flow.flows.0.wavenet.in_layers.2.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.2.parametrizations.weight.original1', 'flow.flows.0.wavenet.in_layers.3.parametrizations.weight.original0', 'flow.flows.0.wavenet.in_layers.3.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.0.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.0.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.1.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.1.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.2.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.2.parametrizations.weight.original1', 'flow.flows.0.wavenet.res_skip_layers.3.parametrizations.weight.original0', 'flow.flows.0.wavenet.res_skip_layers.3.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.0.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.0.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.1.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.1.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.2.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.2.parametrizations.weight.original1', 'flow.flows.1.wavenet.in_layers.3.parametrizations.weight.original0', 'flow.flows.1.wavenet.in_layers.3.parametrizations.weight.original1', 'flow.flows.1.wavenet.res_skip_layers.0.parametrizations.weight.original0', 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'posterior_encoder.wavenet.in_layers.14.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.15.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.15.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.2.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.2.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.3.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.3.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.4.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.4.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.5.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.5.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.6.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.6.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.7.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.7.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.8.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.8.parametrizations.weight.original1', 'posterior_encoder.wavenet.in_layers.9.parametrizations.weight.original0', 'posterior_encoder.wavenet.in_layers.9.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.0.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.0.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.1.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.1.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.10.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.10.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.11.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.11.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.12.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.12.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.13.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.13.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.14.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.14.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.15.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.15.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.2.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.2.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.3.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.3.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.4.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.4.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.5.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.5.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.6.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.6.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.7.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.7.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.8.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.8.parametrizations.weight.original1', 'posterior_encoder.wavenet.res_skip_layers.9.parametrizations.weight.original0', 'posterior_encoder.wavenet.res_skip_layers.9.parametrizations.weight.original1']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"config.json: 100%|██████████| 1.27k/1.27k [00:00<00:00, 4.96MB/s]\n",
"model.safetensors: 100%|██████████| 3.09G/3.09G [03:04<00:00, 16.7MB/s]\n",
"generation_config.json: 100%|██████████| 3.94k/3.94k [00:00<00:00, 21.4MB/s]\n",
"tokenizer_config.json: 100%|██████████| 283k/283k [00:00<00:00, 6.43MB/s]\n",
"vocab.json: 100%|██████████| 1.04M/1.04M [00:00<00:00, 15.4MB/s]\n",
"tokenizer.json: 100%|██████████| 2.48M/2.48M [00:00<00:00, 7.27MB/s]\n",
"merges.txt: 100%|██████████| 494k/494k [00:00<00:00, 16.5MB/s]\n",
"normalizer.json: 100%|██████████| 52.7k/52.7k [00:00<00:00, 223kB/s]\n",
"added_tokens.json: 100%|██████████| 34.6k/34.6k [00:00<00:00, 54.4MB/s]\n",
"special_tokens_map.json: 100%|██████████| 2.07k/2.07k [00:00<00:00, 10.3MB/s]\n",
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
"preprocessor_config.json: 100%|██████████| 340/340 [00:00<00:00, 1.69MB/s]\n"
]
}
],
"source": [
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32\n",
"print(f\"Using {device} with dtype {torch_dtype}\")\n",
"\n",
"model = VitsModel.from_pretrained(\"facebook/mms-tts-zlm\")\n",
"tokenizer = VitsTokenizer.from_pretrained(\"facebook/mms-tts-zlm\")\n",
"\n",
"asr_pipe = pipeline( # noqa: F821\n",
" \"automatic-speech-recognition\",\n",
" model=\"openai/whisper-large-v3\",\n",
" device=device,\n",
" torch_dtype=torch_dtype,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def synthesise(text):\n",
" inputs = tokenizer(text=text, return_tensors=\"pt\")\n",
" input_ids = inputs[\"input_ids\"]\n",
"\n",
" with torch.no_grad():\n",
" outputs = model(input_ids)\n",
"\n",
" speech = outputs[\"waveform\"]\n",
" return speech\n",
"\n",
"def translate(audio):\n",
" outputs = asr_pipe(\n",
" audio,\n",
" max_new_tokens=256,\n",
" generate_kwargs={\"task\": \"transcribe\", \"language\": \"ms\"},\n",
" )\n",
" return outputs[\"text\"]\n",
"\n",
"def speech_to_speech_translation(audio):\n",
" translated_text = translate(audio)\n",
" synthesised_speech = synthesise(translated_text)\n",
" synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)\n",
" return 16000, synthesised_speech"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"x, sr = librosa.load(\"audio.wav\", sr=16_000)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
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"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
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}
],
"source": [
"Audio(x, rate=sr)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Pada bab 16. Saya mungkin telah memberitahu anda tentang permulaan penyelidikan ini dalam beberapa lirik. Tetapi saya mahu anda melihat setiap langkah dengan mana kami datang. Saya juga setuju dengan apa-apa pun yang Marguerite mahukan.\n"
]
},
{
"data": {
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"\n",
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""
]
},
"execution_count": 13,
"metadata": {},
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}
],
"source": [
"text = translate(x)\n",
"print(text)\n",
"tts = synthesise(text)\n",
"\n",
"Audio(tts, rate=16_000)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-8, -8, -5, ..., -4, -2, -3]], dtype=int16)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"(tts.numpy() * 32767).astype(np.int16)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Audio((tts.numpy() * 32767).astype(np.int16), rate=16_000)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sr, x = speech_to_speech_translation(x)\n",
"Audio(x, rate=sr)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}