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"source": [
"# Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers"
]
},
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"source": [
"In this Colab, we present a step-by-step guide on how to fine-tune Whisper \n",
"for any multilingual ASR dataset using Hugging Face 🤗 Transformers. This is a \n",
"more \"hands-on\" version of the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). \n",
"For a more in-depth explanation of Whisper, the Common Voice dataset and the theory behind fine-tuning, the reader is advised to refer to the blog post."
]
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"source": [
"## Introduction"
]
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"source": [
"Whisper is a pre-trained model for automatic speech recognition (ASR) \n",
"published in [September 2022](https://openai.com/blog/whisper/) by the authors \n",
"Alec Radford et al. from OpenAI. Unlike many of its predecessors, such as \n",
"[Wav2Vec 2.0](https://arxiv.org/abs/2006.11477), which are pre-trained \n",
"on un-labelled audio data, Whisper is pre-trained on a vast quantity of \n",
"**labelled** audio-transcription data, 680,000 hours to be precise. \n",
"This is an order of magnitude more data than the un-labelled audio data used \n",
"to train Wav2Vec 2.0 (60,000 hours). What is more, 117,000 hours of this \n",
"pre-training data is multilingual ASR data. This results in checkpoints \n",
"that can be applied to over 96 languages, many of which are considered \n",
"_low-resource_.\n",
"\n",
"When scaled to 680,000 hours of labelled pre-training data, Whisper models \n",
"demonstrate a strong ability to generalise to many datasets and domains.\n",
"The pre-trained checkpoints achieve competitive results to state-of-the-art \n",
"ASR systems, with near 3% word error rate (WER) on the test-clean subset of \n",
"LibriSpeech ASR and a new state-of-the-art on TED-LIUM with 4.7% WER (_c.f._ \n",
"Table 8 of the [Whisper paper](https://cdn.openai.com/papers/whisper.pdf)).\n",
"The extensive multilingual ASR knowledge acquired by Whisper during pre-training \n",
"can be leveraged for other low-resource languages; through fine-tuning, the \n",
"pre-trained checkpoints can be adapted for specific datasets and languages \n",
"to further improve upon these results. We'll show just how Whisper can be fine-tuned \n",
"for low-resource languages in this Colab."
]
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"source": [
"\n",
"\n",
"Figure 1: Whisper model. The architecture \n",
"follows the standard Transformer-based encoder-decoder model. A \n",
"log-Mel spectrogram is input to the encoder. The last encoder \n",
"hidden states are input to the decoder via cross-attention mechanisms. The \n",
"decoder autoregressively predicts text tokens, jointly conditional on the \n",
"encoder hidden states and previously predicted tokens. Figure source: \n",
"OpenAI Whisper Blog.\n",
""
]
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"source": [
"The Whisper checkpoints come in five configurations of varying model sizes.\n",
"The smallest four are trained on either English-only or multilingual data.\n",
"The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints \n",
"are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The \n",
"checkpoints are summarised in the following table with links to the models on the Hub:\n",
"\n",
"| Size | Layers | Width | Heads | Parameters | English-only | Multilingual |\n",
"|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|\n",
"| tiny | 4 | 384 | 6 | 39 M | [âś“](https://huggingface.co/openai/whisper-tiny.en) | [âś“](https://huggingface.co/openai/whisper-tiny.) |\n",
"| base | 6 | 512 | 8 | 74 M | [âś“](https://huggingface.co/openai/whisper-base.en) | [âś“](https://huggingface.co/openai/whisper-base) |\n",
"| small | 12 | 768 | 12 | 244 M | [âś“](https://huggingface.co/openai/whisper-small.en) | [âś“](https://huggingface.co/openai/whisper-small) |\n",
"| medium | 24 | 1024 | 16 | 769 M | [âś“](https://huggingface.co/openai/whisper-medium.en) | [âś“](https://huggingface.co/openai/whisper-medium) |\n",
"| large | 32 | 1280 | 20 | 1550 M | x | [âś“](https://huggingface.co/openai/whisper-large) |\n",
"\n",
"For demonstration purposes, we'll fine-tune the multilingual version of the \n",
"[`\"small\"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB). \n",
"As for our data, we'll train and evaluate our system on a low-resource language \n",
"taken from the [Common Voice](https://huggingface.co/datasets/mozilla-foundation/fleurs_11_0)\n",
"dataset. We'll show that with as little as 8 hours of fine-tuning data, we can achieve \n",
"strong performance in this language."
]
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"source": [
"------------------------------------------------------------------------\n",
"\n",
"\\\\({}^1\\\\) The name Whisper follows from the acronym “WSPSR”, which stands for “Web-scale Supervised Pre-training for Speech Recognition”."
]
},
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"source": [
"## Load Dataset\n",
"Loading MS-MY Dataset from FLEURS.\n",
"Combine train and validation set."
]
},
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"Downloading builder script: 0%| | 0.00/12.8k [00:00\n",
"\n",
"Figure 2: Conversion of sampled audio array to log-Mel spectrogram.\n",
"Left: sampled 1-dimensional audio signal. Right: corresponding log-Mel spectrogram. Figure source:\n",
"Google SpecAugment Blog.\n",
""
]
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"source": [
"We'll load the feature extractor from the pre-trained checkpoint with the default values:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
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"text/plain": [
"Downloading: 0%| | 0.00/185k [00:00 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9ae9c365",
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{
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"text/plain": [
" 0%| | 0/2929 [00:00 Dict[str, torch.Tensor]:\n",
" # split inputs and labels since they have to be of different lengths and need different padding methods\n",
" # first treat the audio inputs by simply returning torch tensors\n",
" input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
" batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
"\n",
" # get the tokenized label sequences\n",
" label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
" # pad the labels to max length\n",
" labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
"\n",
" # replace padding with -100 to ignore loss correctly\n",
" labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
"\n",
" # if bos token is appended in previous tokenization step,\n",
" # cut bos token here as it's append later anyways\n",
" if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
" labels = labels[:, 1:]\n",
"\n",
" batch[\"labels\"] = labels\n",
"\n",
" return batch"
]
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"source": [
"Let's initialise the data collator we've just defined:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
"metadata": {
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"source": [
"data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
]
},
{
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"id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
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"source": [
"### Evaluation Metrics"
]
},
{
"cell_type": "markdown",
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"source": [
"We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
"ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b22b4011-f31f-4b57-b684-c52332f92890",
"metadata": {
"id": "b22b4011-f31f-4b57-b684-c52332f92890"
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"text/plain": [
"Downloading builder script: 0%| | 0.00/4.49k [00:00\n",
" \n",
" \n",
" [1001/5000 1:05:39 < 4:22:51, 0.25 it/s, Epoch 2.86/15]\n",
" \n",
"
![\"Trulli\"](\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/whisper_architecture.svg\")
\n",
"![\"Trulli\"](\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/spectrogram.jpg\")
\n",
"