\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",
" "
]
},
{
"cell_type": "markdown",
"id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa",
"metadata": {
"id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"
},
"source": [
"We'll load the feature extractor from the pre-trained checkpoint with the default values:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
"metadata": {
"id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5"
},
"outputs": [],
"source": [
"from transformers import WhisperFeatureExtractor\n",
"\n",
"feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-large-v2\")"
]
},
{
"cell_type": "markdown",
"id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb",
"metadata": {
"id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb"
},
"source": [
"### Load WhisperTokenizer"
]
},
{
"cell_type": "markdown",
"id": "2bc82609-a9fb-447a-a2af-99597c864029",
"metadata": {
"id": "2bc82609-a9fb-447a-a2af-99597c864029"
},
"source": [
"The Whisper model outputs a sequence of _token ids_. The tokenizer maps each of these token ids to their corresponding text string. For Hindi, we can load the pre-trained tokenizer and use it for fine-tuning without any further modifications. We simply have to \n",
"specify the target language and the task. These arguments inform the \n",
"tokenizer to prefix the language and task tokens to the start of encoded \n",
"label sequences:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
"metadata": {
"id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
"outputId": "5c004b44-86e7-4e00-88be-39e0af5eed69"
},
"outputs": [],
"source": [
"from transformers import WhisperTokenizer\n",
"\n",
"tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-large-v2\", language=\"Chinese\", task=\"transcribe\")"
]
},
{
"cell_type": "markdown",
"id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b",
"metadata": {
"id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"
},
"source": [
"### Combine To Create A WhisperProcessor"
]
},
{
"cell_type": "markdown",
"id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d",
"metadata": {
"id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d"
},
"source": [
"To simplify using the feature extractor and tokenizer, we can _wrap_ \n",
"both into a single `WhisperProcessor` class. This processor object \n",
"inherits from the `WhisperFeatureExtractor` and `WhisperProcessor`, \n",
"and can be used on the audio inputs and model predictions as required. \n",
"In doing so, we only need to keep track of two objects during training: \n",
"the `processor` and the `model`:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
"metadata": {
"id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
},
"outputs": [],
"source": [
"from transformers import WhisperProcessor\n",
"\n",
"processor = WhisperProcessor.from_pretrained(\"openai/whisper-large-v2\", language=\"Chinese\", task=\"transcribe\")"
]
},
{
"cell_type": "markdown",
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
"metadata": {
"id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
},
"source": [
"### Prepare Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c69246a2",
"metadata": {},
"outputs": [],
"source": [
"from datasets import Audio\n",
"\n",
"cv = cv.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
"# fleurs = fleurs.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
"# lbv = lbv.cast_column(\"audio\", Audio(sampling_rate=16000))"
]
},
{
"cell_type": "markdown",
"id": "3df7378a-a4c0-45d7-8d07-defbd1062ab6",
"metadata": {},
"source": [
"We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d041650e-1c48-4439-87b3-5b6f4a514107",
"metadata": {},
"outputs": [],
"source": [
"from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
"\n",
"do_lower_case = False\n",
"do_remove_punctuation = False\n",
"\n",
"normalizer = BasicTextNormalizer()"
]
},
{
"cell_type": "markdown",
"id": "89e12c2e-2f14-479b-987b-f0c75c881095",
"metadata": {},
"source": [
"Now we can write a function to prepare our data ready for the model:\n",
"1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
"2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
"3. We perform any optional pre-processing (lower-case or remove punctuation).\n",
"4. We encode the transcriptions to label ids through the use of the tokenizer."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4c79b333",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift\n",
"\n",
"augment_waveform = Compose([\n",
"# AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=0.3),\n",
" TimeStretch(min_rate=0.9, max_rate=1.25, p=0.3, leave_length_unchanged=False),\n",
"# PitchShift(min_semitones=-4, max_semitones=4, p=0.3),\n",
" ])\n",
"\n",
"def augment_dataset(batch):\n",
"\n",
" audio = batch[\"audio\"][\"array\"]\n",
" # apply augmentation\n",
" augmented_audio = augment_waveform(samples=audio, sample_rate=16000)\n",
"\n",
" batch[\"audio\"][\"array\"] = augmented_audio\n",
"\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c085911c-a10a-41ef-8874-306e0503e9bb",
"metadata": {},
"outputs": [],
"source": [
"def prepare_dataset(batch):\n",
" # load and (possibly) resample audio data to 16kHz\n",
" audio = batch[\"audio\"]\n",
"\n",
" # compute log-Mel input features from input audio array \n",
" batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
" # compute input length of audio sample in seconds\n",
" batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
" \n",
" # optional pre-processing steps\n",
" transcription = batch[\"transcription\"]\n",
" if do_lower_case:\n",
" transcription = transcription.lower()\n",
" if do_remove_punctuation:\n",
" transcription = normalizer(transcription).strip()\n",
" \n",
" # encode target text to label ids\n",
" batch[\"labels\"] = processor.tokenizer(transcription).input_ids\n",
" return batch"
]
},
{
"cell_type": "markdown",
"id": "8c960965-9fb6-466f-9dbd-c9d43e71d9d0",
"metadata": {
"id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
},
"source": [
"We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 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": 11,
"id": "db271164",
"metadata": {},
"outputs": [
{
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"model_id": "f9683eb693c449c19719183b46b54fbb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/5296 [00:00, ?ex/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# fleurs['train'] = fleurs['train'].map(augment_dataset)\n",
"cv['train'] = cv['train'].map(augment_dataset)\n",
"# lbv['train'] = lbv['train'].map(augment_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b459b0c5",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7bd2c16519ff4309a00299e19cce5365",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/5296 [00:00, ?ex/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "82fcd95608b24d5794fa063e7d3686bb",
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"version_minor": 0
},
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" 0%| | 0/2438 [00:00, ?ex/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# fleurs = fleurs.map(prepare_dataset, remove_columns=fleurs.column_names['train'], num_proc=1).with_format(\"torch\")\n",
"cv = cv.map(prepare_dataset, remove_columns=cv.column_names['train'], num_proc=1).with_format(\"torch\")\n",
"# lbv = lbv.map(prepare_dataset, remove_columns=lbv.column_names['train'], num_proc=1).with_format(\"torch\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "e9034b52",
"metadata": {},
"outputs": [],
"source": [
"# from datasets import concatenate_datasets\n",
"\n",
"# cc = DatasetDict()\n",
"# cc['train'] = concatenate_datasets([fleurs['train'], cv['train'], lbv['train']])\n",
"# cc['test'] = cv['test']"
]
},
{
"cell_type": "markdown",
"id": "54ce0fdb-7218-4a4d-b175-383980fec0df",
"metadata": {},
"source": [
"Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "01cb25ef-4bb0-4325-9461-f59198acadf6",
"metadata": {},
"outputs": [],
"source": [
"max_input_length = 30.0\n",
"\n",
"def is_audio_in_length_range(length):\n",
" return length < max_input_length"
]
},
{
"cell_type": "markdown",
"id": "30e676a8-7ca8-4850-8c5d-5b2b00d13fba",
"metadata": {},
"source": [
"We apply our filter function to all samples of our training dataset through 🤗 Datasets' `.filter` method:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "333f7f6e-6053-4d3b-8924-c733c79b82ac",
"metadata": {},
"outputs": [
{
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"model_id": "518997e4f5324bdf82b4906be1c550c9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/6 [00:00, ?ba/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cv['train'] = cv['train'].filter(\n",
" is_audio_in_length_range,\n",
" input_columns=[\"input_length\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
"metadata": {
"id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
},
"source": [
"## Training and Evaluation"
]
},
{
"cell_type": "markdown",
"id": "a693e768-c5a6-453f-89a1-b601dcf7daf7",
"metadata": {
"id": "a693e768-c5a6-453f-89a1-b601dcf7daf7"
},
"source": [
"Now that we've prepared our data, we're ready to dive into the training pipeline. \n",
"The [🤗 Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)\n",
"will do much of the heavy lifting for us. All we have to do is:\n",
"\n",
"- Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.\n",
"\n",
"- Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.\n",
"\n",
"- Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.\n",
"\n",
"- Define the training configuration: this will be used by the 🤗 Trainer to define the training schedule.\n",
"\n",
"Once we've fine-tuned the model, we will evaluate it on the test data to verify that we have correctly trained it \n",
"to transcribe speech in Hindi."
]
},
{
"cell_type": "markdown",
"id": "8d230e6d-624c-400a-bbf5-fa660881df25",
"metadata": {
"id": "8d230e6d-624c-400a-bbf5-fa660881df25"
},
"source": [
"### Define a Data Collator"
]
},
{
"cell_type": "markdown",
"id": "04def221-0637-4a69-b242-d3f0c1d0ee78",
"metadata": {
"id": "04def221-0637-4a69-b242-d3f0c1d0ee78"
},
"source": [
"The data collator for a sequence-to-sequence speech model is unique in the sense that it \n",
"treats the `input_features` and `labels` independently: the `input_features` must be \n",
"handled by the feature extractor and the `labels` by the tokenizer.\n",
"\n",
"The `input_features` are already padded to 30s and converted to a log-Mel spectrogram \n",
"of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`\n",
"to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.\n",
"\n",
"The `labels` on the other hand are un-padded. We first pad the sequences\n",
"to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens \n",
"are then replaced by `-100` so that these tokens are **not** taken into account when \n",
"computing the loss. We then cut the BOS token from the start of the label sequence as we \n",
"append it later during training.\n",
"\n",
"We can leverage the `WhisperProcessor` we defined earlier to perform both the \n",
"feature extractor and the tokenizer operations:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
"metadata": {
"id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
},
"outputs": [],
"source": [
"import torch\n",
"\n",
"from dataclasses import dataclass\n",
"from typing import Any, Dict, List, Union\n",
"\n",
"@dataclass\n",
"class DataCollatorSpeechSeq2SeqWithPadding:\n",
" processor: Any\n",
"\n",
" def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> 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"
]
},
{
"cell_type": "markdown",
"id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
"metadata": {
"id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
},
"source": [
"Let's initialise the data collator we've just defined:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
"metadata": {
"id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
},
"outputs": [],
"source": [
"data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
]
},
{
"cell_type": "markdown",
"id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
"metadata": {
"id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
},
"source": [
"### Evaluation Metrics"
]
},
{
"cell_type": "markdown",
"id": "66fee1a7-a44c-461e-b047-c3917221572e",
"metadata": {
"id": "66fee1a7-a44c-461e-b047-c3917221572e"
},
"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": 21,
"id": "b22b4011-f31f-4b57-b684-c52332f92890",
"metadata": {
"id": "b22b4011-f31f-4b57-b684-c52332f92890"
},
"outputs": [
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "2da06d3741bf4cee961e3352d32a2515",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading builder script: 0%| | 0.00/5.60k [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import evaluate\n",
"\n",
"# wer_metric = evaluate.load(\"wer\")\n",
"cer_metric = evaluate.load(\"cer\")"
]
},
{
"cell_type": "markdown",
"id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508",
"metadata": {
"id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
},
"source": [
"We then simply have to define a function that takes our model \n",
"predictions and returns the WER metric. This function, called\n",
"`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
"in the `label_ids` (undoing the step we applied in the \n",
"data collator to ignore padded tokens correctly in the loss).\n",
"It then decodes the predicted and label ids to strings. Finally,\n",
"it computes the WER between the predictions and reference labels. \n",
"Here, we have the option of evaluating with the 'normalised' transcriptions \n",
"and predictions. We recommend you set this to `True` to benefit from the WER \n",
"improvement obtained by normalising the transcriptions."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52",
"metadata": {
"id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
},
"outputs": [],
"source": [
"#Â evaluate with the 'normalised' WER\n",
"do_normalize_eval = True\n",
"\n",
"def compute_metrics(pred):\n",
" pred_ids = pred.predictions\n",
" label_ids = pred.label_ids\n",
"\n",
" # replace -100 with the pad_token_id\n",
" label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
"\n",
" # we do not want to group tokens when computing the metrics\n",
" pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
" label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
"\n",
" if do_normalize_eval:\n",
" pred_str = [normalizer(pred) for pred in pred_str]\n",
" label_str = [normalizer(label) for label in label_str]\n",
"\n",
"# wer = 100 * wer_metric.compute(predictions=pred_str, references=label_str)\n",
" cer = 100 * cer_metric.compute(predictions=pred_str, references=label_str)\n",
"\n",
" return {\"cer\": cer}"
]
},
{
"cell_type": "markdown",
"id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
"metadata": {
"id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
},
"source": [
"###Â Load a Pre-Trained Checkpoint"
]
},
{
"cell_type": "markdown",
"id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
"metadata": {
"id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
},
"source": [
"Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
"is trivial through use of 🤗 Transformers!"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
"metadata": {
"id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f"
},
"outputs": [
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{
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],
"source": [
"from transformers import WhisperForConditionalGeneration\n",
"\n",
"model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-large-v2\")"
]
},
{
"cell_type": "markdown",
"id": "a15ead5f-2277-4a39-937b-585c2497b2df",
"metadata": {
"id": "a15ead5f-2277-4a39-937b-585c2497b2df"
},
"source": [
"Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)). Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
"metadata": {
"id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
},
"outputs": [],
"source": [
"model.config.forced_decoder_ids = None\n",
"model.config.suppress_tokens = []\n",
"model.config.use_cache = False"
]
},
{
"cell_type": "markdown",
"id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
"metadata": {
"id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
},
"source": [
"### Define the Training Configuration"
]
},
{
"cell_type": "markdown",
"id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
"metadata": {
"id": "c21af1e9-0188-4134-ac82-defc7bdcc436"
},
"source": [
"In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
"metadata": {
"id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"PyTorch: setting up devices\n"
]
}
],
"source": [
"from transformers import Seq2SeqTrainingArguments\n",
"\n",
"training_args = Seq2SeqTrainingArguments(\n",
" output_dir=\"./\",\n",
" per_device_train_batch_size=8,\n",
" gradient_accumulation_steps=4, # increase by 2x for every 2x decrease in batch size\n",
" learning_rate=1e-5,\n",
" warmup_steps=100,\n",
" max_steps=1000,\n",
" gradient_checkpointing=True,\n",
" fp16=True,\n",
" evaluation_strategy=\"steps\",\n",
" per_device_eval_batch_size=4,\n",
" predict_with_generate=True,\n",
" generation_max_length=225,\n",
" save_steps=200,\n",
" eval_steps=200,\n",
" logging_steps=25,\n",
" report_to=[\"tensorboard\"],\n",
" load_best_model_at_end=True,\n",
" metric_for_best_model=\"cer\",\n",
" greater_is_better=False,\n",
" push_to_hub=True,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b3a944d8-3112-4552-82a0-be25988b3857",
"metadata": {
"id": "b3a944d8-3112-4552-82a0-be25988b3857"
},
"source": [
"**Note**: if one does not want to upload the model checkpoints to the Hub, \n",
"set `push_to_hub=False`."
]
},
{
"cell_type": "markdown",
"id": "bac29114-d226-4f54-97cf-8718c9f94e1e",
"metadata": {
"id": "bac29114-d226-4f54-97cf-8718c9f94e1e"
},
"source": [
"We can forward the training arguments to the 🤗 Trainer along with our model,\n",
"dataset, data collator and `compute_metrics` function:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "d546d7fe-0543-479a-b708-2ebabec19493",
"metadata": {
"id": "d546d7fe-0543-479a-b708-2ebabec19493"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/whisper-large-v2-yue/./ is already a clone of https://huggingface.co/Scrya/whisper-large-v2-yue. Make sure you pull the latest changes with `repo.git_pull()`.\n",
"max_steps is given, it will override any value given in num_train_epochs\n",
"Using cuda_amp half precision backend\n"
]
}
],
"source": [
"from transformers import Seq2SeqTrainer\n",
"\n",
"trainer = Seq2SeqTrainer(\n",
" args=training_args,\n",
" model=model,\n",
" train_dataset=cv['train'],\n",
" eval_dataset=cv['test'],\n",
" data_collator=data_collator,\n",
" compute_metrics=compute_metrics,\n",
" tokenizer=processor.feature_extractor,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "uOrRhDGtN5S4",
"metadata": {
"id": "uOrRhDGtN5S4"
},
"source": [
"We'll save the processor object once before starting training. Since the processor is not trainable, it won't change over the course of training:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "-2zQwMfEOBJq",
"metadata": {
"id": "-2zQwMfEOBJq"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Feature extractor saved in ./preprocessor_config.json\n",
"tokenizer config file saved in ./tokenizer_config.json\n",
"Special tokens file saved in ./special_tokens_map.json\n",
"added tokens file saved in ./added_tokens.json\n"
]
}
],
"source": [
"processor.save_pretrained(training_args.output_dir)"
]
},
{
"cell_type": "markdown",
"id": "7f404cf9-4345-468c-8196-4bd101d9bd51",
"metadata": {
"id": "7f404cf9-4345-468c-8196-4bd101d9bd51"
},
"source": [
"### Training"
]
},
{
"cell_type": "markdown",
"id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112",
"metadata": {
"id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112"
},
"source": [
"Training will take approximately 5-10 hours depending on your GPU. The peak GPU memory for the given training configuration is approximately 36GB. \n",
"Depending on your GPU, it is possible that you will encounter a CUDA `\"out-of-memory\"` error when you launch training. \n",
"In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 \n",
"and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)\n",
"to compensate.\n",
"\n",
"To launch training, simply execute:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
"metadata": {
"id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following columns in the training set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n",
"***** Running training *****\n",
" Num examples = 5296\n",
" Num Epochs = 7\n",
" Instantaneous batch size per device = 8\n",
" Total train batch size (w. parallel, distributed & accumulation) = 32\n",
" Gradient Accumulation steps = 4\n",
" Total optimization steps = 1000\n",
" Number of trainable parameters = 1543304960\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" [1000/1000 5:14:33, Epoch 6/7]\n",
"
\n",
" \n",
" \n",
" \n",
" Step \n",
" Training Loss \n",
" Validation Loss \n",
" Cer \n",
" \n",
" \n",
" \n",
" \n",
" 200 \n",
" 0.112600 \n",
" 0.166591 \n",
" 7.310261 \n",
" \n",
" \n",
" 400 \n",
" 0.046700 \n",
" 0.160966 \n",
" 6.941915 \n",
" \n",
" \n",
" 600 \n",
" 0.021700 \n",
" 0.162111 \n",
" 6.387371 \n",
" \n",
" \n",
" 800 \n",
" 0.008000 \n",
" 0.169945 \n",
" 6.306416 \n",
" \n",
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""
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},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 2438\n",
" Batch size = 4\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"/home/ubuntu/hf_env/lib/python3.8/site-packages/transformers/generation/utils.py:1134: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
" warnings.warn(\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
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{
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"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
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" ],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
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"\n",
"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"\n"
]
},
{
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"text": [
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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"Generate config GenerationConfig {\n",
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"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Saving model checkpoint to ./checkpoint-200\n",
"Configuration saved in ./checkpoint-200/config.json\n",
"Model weights saved in ./checkpoint-200/pytorch_model.bin\n",
"Feature extractor saved in ./checkpoint-200/preprocessor_config.json\n",
"Feature extractor saved in ./preprocessor_config.json\n",
"The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 2438\n",
" Batch size = 4\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
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"\n",
"Generate config GenerationConfig {\n",
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" \"eos_token_id\": 50257,\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" \"decoder_start_token_id\": 50258,\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n"
]
},
{
"name": "stderr",
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"text": [
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
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"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
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" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Saving model checkpoint to ./checkpoint-400\n",
"Configuration saved in ./checkpoint-400/config.json\n",
"Model weights saved in ./checkpoint-400/pytorch_model.bin\n",
"Feature extractor saved in ./checkpoint-400/preprocessor_config.json\n",
"Feature extractor saved in ./preprocessor_config.json\n",
"The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 2438\n",
" Batch size = 4\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" ],\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
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" ],\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n"
]
},
{
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"Generate config GenerationConfig {\n",
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" 50257\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
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" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
" ],\n",
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"Generate config GenerationConfig {\n",
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"}\n",
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"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 50257\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" ],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 50257\n",
" ],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
" ],\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
" ],\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" ],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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" \"use_cache\": false\n",
"}\n",
"\n"
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},
{
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"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
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"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
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" \"use_cache\": false\n",
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"\n",
"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving model checkpoint to ./checkpoint-600\n",
"Configuration saved in ./checkpoint-600/config.json\n",
"Model weights saved in ./checkpoint-600/pytorch_model.bin\n",
"Feature extractor saved in ./checkpoint-600/preprocessor_config.json\n",
"Feature extractor saved in ./preprocessor_config.json\n",
"The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 2438\n",
" Batch size = 4\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
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"Generate config GenerationConfig {\n",
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"Generate config GenerationConfig {\n",
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"\n"
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"Generate config GenerationConfig {\n",
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"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
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"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Saving model checkpoint to ./checkpoint-800\n",
"Configuration saved in ./checkpoint-800/config.json\n",
"Model weights saved in ./checkpoint-800/pytorch_model.bin\n",
"Feature extractor saved in ./checkpoint-800/preprocessor_config.json\n",
"Feature extractor saved in ./preprocessor_config.json\n",
"The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n",
"***** Running Evaluation *****\n",
" Num examples = 2438\n",
" Batch size = 4\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
" ],\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n"
]
},
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"\n",
"Generate config GenerationConfig {\n",
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"\n",
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"\n",
"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"\n",
"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
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" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
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" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
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" ],\n",
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" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
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" \"bos_token_id\": 50257,\n",
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" \"pad_token_id\": 50257,\n",
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{
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"\n",
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"}\n",
"\n"
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{
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"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
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" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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"}\n",
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"Generate config GenerationConfig {\n",
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"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
" 50257\n",
" ],\n",
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"}\n",
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" \"suppress_tokens\": [],\n",
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"Generate config GenerationConfig {\n",
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" 50257\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 50257\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" \"suppress_tokens\": [],\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
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" 220,\n",
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" ],\n",
" \"bos_token_id\": 50257,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
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" \"suppress_tokens\": [],\n",
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"Generate config GenerationConfig {\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
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" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n"
]
},
{
"name": "stderr",
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"text": [
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
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" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"bos_token_id\": 50257,\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
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" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
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"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
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" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Generate config GenerationConfig {\n",
" \"begin_suppress_tokens\": [\n",
" 220,\n",
" 50257\n",
" ],\n",
" \"bos_token_id\": 50257,\n",
" \"decoder_start_token_id\": 50258,\n",
" \"eos_token_id\": 50257,\n",
" \"max_length\": 448,\n",
" \"pad_token_id\": 50257,\n",
" \"suppress_tokens\": [],\n",
" \"transformers_version\": \"4.26.0.dev0\",\n",
" \"use_cache\": false\n",
"}\n",
"\n",
"Saving model checkpoint to ./checkpoint-1000\n",
"Configuration saved in ./checkpoint-1000/config.json\n",
"Model weights saved in ./checkpoint-1000/pytorch_model.bin\n",
"Feature extractor saved in ./checkpoint-1000/preprocessor_config.json\n",
"Feature extractor saved in ./preprocessor_config.json\n",
"\n",
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
"Loading best model from ./checkpoint-1000 (score: 6.213317142278891).\n"
]
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=1000, training_loss=0.15269774760305882, metrics={'train_runtime': 18880.3694, 'train_samples_per_second': 1.695, 'train_steps_per_second': 0.053, 'total_flos': 6.81452970835968e+19, 'train_loss': 0.15269774760305882, 'epoch': 6.06})"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "markdown",
"id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3",
"metadata": {
"id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3"
},
"source": [
"We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "c704f91e-241b-48c9-b8e0-f0da396a9663",
"metadata": {
"id": "c704f91e-241b-48c9-b8e0-f0da396a9663"
},
"outputs": [],
"source": [
"kwargs = {\n",
" \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
" \"dataset\": \"mozilla-foundation/common_voice_11_0\", # a 'pretty' name for the training dataset\n",
" \"language\": \"yue\",\n",
" \"model_name\": \"Whisper Large V2 - Cantonese - Augmented\", # a 'pretty' name for your model\n",
" \"finetuned_from\": \"openai/whisper-large-v2\",\n",
" \"tasks\": \"automatic-speech-recognition\",\n",
" \"tags\": \"whisper-event\",\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "090d676a-f944-4297-a938-a40eda0b2b68",
"metadata": {
"id": "090d676a-f944-4297-a938-a40eda0b2b68"
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"source": [
"The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command and save the preprocessor object we created:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d7030622-caf7-4039-939b-6195cdaa2585",
"metadata": {
"id": "d7030622-caf7-4039-939b-6195cdaa2585"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving model checkpoint to ./\n",
"Configuration saved in ./config.json\n",
"Model weights saved in ./pytorch_model.bin\n",
"Feature extractor saved in ./preprocessor_config.json\n",
"Several commits (2) will be pushed upstream.\n",
"The progress bars may be unreliable.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0e2d9a13a7f143fcb814130c727cebe0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Upload file pytorch_model.bin: 0%| | 1.00/5.75G [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "7daa9c192f004d8391c9c8fe96f3a757",
"version_major": 2,
"version_minor": 0
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"text/plain": [
"Upload file runs/Dec19_12-09-59_129-146-74-165/events.out.tfevents.1671451847.129-146-74-165.127332.2: 0%| …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trainer.push_to_hub(**kwargs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e19f35cf",
"metadata": {},
"outputs": [],
"source": [
"fleurs_results = trainer.evaluate(fleurs['test'])\n",
"print(fleurs_results)\n",
"\n",
"cv_results = trainer.evaluate(cv['test'])\n",
"print(cv_results)\n",
"\n",
"lbv_results = trainer.evaluate(lbv['test'])\n",
"print(lbv_results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c1e53d0",
"metadata": {},
"outputs": [],
"source": [
"evaluate.push_to_hub(\n",
" model_id='Scrya/whisper-medium-id',\n",
" metric_value=round(fleurs_results['eval_wer'], 2),\n",
" metric_type=\"wer\",\n",
" metric_name=\"WER\",\n",
" dataset_name='google/fleurs',\n",
" dataset_type='google/fleurs',\n",
" dataset_split='test',\n",
" dataset_config='id_id',\n",
" task_type=\"automatic-speech-recognition\",\n",
" task_name=\"Automatic Speech Recognition\",\n",
" overwrite=True\n",
" )\n",
"\n",
"evaluate.push_to_hub(\n",
" model_id='Scrya/whisper-medium-id',\n",
" metric_value=round(fleurs_results['eval_cer'], 2),\n",
" metric_type=\"cer\",\n",
" metric_name=\"CER\",\n",
" dataset_name='google/fleurs',\n",
" dataset_type='google/fleurs',\n",
" dataset_split='test',\n",
" dataset_config='id_id',\n",
" task_type=\"automatic-speech-recognition\",\n",
" task_name=\"Automatic Speech Recognition\",\n",
" overwrite=True\n",
" )\n",
"\n",
"evaluate.push_to_hub(\n",
" model_id='Scrya/whisper-medium-id',\n",
" metric_value=round(cv_results['eval_wer'], 2),\n",
" metric_type=\"wer\",\n",
" metric_name=\"WER\",\n",
" dataset_name='mozilla-foundation/common_voice_11_0',\n",
" dataset_type='mozilla-foundation/common_voice_11_0',\n",
" dataset_split='test',\n",
" dataset_config='id',\n",
" task_type=\"automatic-speech-recognition\",\n",
" task_name=\"Automatic Speech Recognition\",\n",
" overwrite=True\n",
" )\n",
"\n",
"evaluate.push_to_hub(\n",
" model_id='Scrya/whisper-medium-id',\n",
" metric_value=round(cv_results['eval_cer'], 2),\n",
" metric_type=\"cer\",\n",
" metric_name=\"CER\",\n",
" dataset_name='mozilla-foundation/common_voice_11_0',\n",
" dataset_type='mozilla-foundation/common_voice_11_0',\n",
" dataset_split='test',\n",
" dataset_config='id',\n",
" task_type=\"automatic-speech-recognition\",\n",
" task_name=\"Automatic Speech Recognition\",\n",
" overwrite=True\n",
" )\n",
"\n",
"evaluate.push_to_hub(\n",
" model_id='Scrya/whisper-medium-id',\n",
" metric_value=round(lbv_results['eval_wer'], 2),\n",
" metric_type=\"wer\",\n",
" metric_name=\"WER\",\n",
" dataset_name='indonesian-nlp/librivox-indonesia',\n",
" dataset_type='indonesian-nlp/librivox-indonesia',\n",
" dataset_split='test',\n",
" dataset_config='ind',\n",
" task_type=\"automatic-speech-recognition\",\n",
" task_name=\"Automatic Speech Recognition\",\n",
" overwrite=True\n",
" )\n",
"\n",
"evaluate.push_to_hub(\n",
" model_id='Scrya/whisper-medium-id',\n",
" metric_value=round(lbv_results['eval_cer'], 2),\n",
" metric_type=\"cer\",\n",
" metric_name=\"CER\",\n",
" dataset_name='indonesian-nlp/librivox-indonesia',\n",
" dataset_type='indonesian-nlp/librivox-indonesia',\n",
" dataset_split='test',\n",
" dataset_config='ind',\n",
" task_type=\"automatic-speech-recognition\",\n",
" task_name=\"Automatic Speech Recognition\",\n",
" overwrite=True\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "ca743fbd-602c-48d4-ba8d-a2fe60af64ba",
"metadata": {
"id": "ca743fbd-602c-48d4-ba8d-a2fe60af64ba"
},
"source": [
"## Closing Remarks"
]
},
{
"cell_type": "markdown",
"id": "7f737783-2870-4e35-aa11-86a42d7d997a",
"metadata": {
"id": "7f737783-2870-4e35-aa11-86a42d7d997a"
},
"source": [
"In this blog, we covered a step-by-step guide on fine-tuning Whisper for multilingual ASR \n",
"using 🤗 Datasets, Transformers and the Hugging Face Hub. For more details on the Whisper model, the Common Voice dataset and the theory behind fine-tuning, refere to the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). If you're interested in fine-tuning other \n",
"Transformers models, both for English and multilingual ASR, be sure to check out the \n",
"examples scripts at [examples/pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition)."
]
}
],
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