Commit
•
2efab78
1
Parent(s):
ff7cbef
Initial commit
Browse files- .gitignore +6 -0
- README.md +129 -0
- added_tokens.json +109 -0
- config.json +41 -0
- merges.txt +0 -0
- preprocessor_config.json +0 -0
- pytorch_model.bin +3 -0
- results_cv11_greedy/eval_results.txt +2 -0
- results_cv11_greedy/log_predictions.txt +0 -0
- results_cv11_greedy/log_targets.txt +0 -0
- runs/Dec15_23-23-05_129-213-84-64/1671146607.251786/events.out.tfevents.1671146607.129-213-84-64.2610414.1 +3 -0
- runs/Dec15_23-23-05_129-213-84-64/events.out.tfevents.1671146607.129-213-84-64.2610414.0 +3 -0
- runs/Dec15_23-23-05_129-213-84-64/events.out.tfevents.1671212353.129-213-84-64.2610414.2 +3 -0
- special_tokens_map.json +133 -0
- tokenizer_config.json +36 -0
- vocab.json +0 -0
.gitignore
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checkpoint-*/
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*results.json
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training_args.bin
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trainer_state.json
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normalizer.json
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wer_summary/
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README.md
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---
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license: apache-2.0
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language: de
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library_name: transformers
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thumbnail: null
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tags:
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- automatic-speech-recognition
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- whisper-event
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datasets:
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- mozilla-foundation/common_voice_11_0
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metrics:
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- wer
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model-index:
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- name: Fine-tuned whisper-small model for ASR in German
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice 11.0
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type: mozilla-foundation/common_voice_11_0
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config: de
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split: test
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args: de
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metrics:
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- name: WER (Greedy)
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type: wer
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value: 11.35
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---
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<style>
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img {
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display: inline;
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}
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</style>
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![Model architecture](https://img.shields.io/badge/Model_Architecture-seq2seq-lightgrey)
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![Model size](https://img.shields.io/badge/Params-244M-lightgrey)
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![Language](https://img.shields.io/badge/Language-German-lightgrey)
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# Fine-tuned whisper-small model for ASR in German
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small), trained on the mozilla-foundation/common_voice_11_0 de dataset. When using the model make sure that your speech input is also sampled at 16Khz. **This model also predicts casing and punctuation.**
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## Usage
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Inference with 🤗 Pipeline
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```python
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import torch
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from datasets import load_dataset
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from transformers import pipeline
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Load pipeline
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pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-small-cv11-german-punct", device=device)
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# NB: set forced_decoder_ids for generation utils
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pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="de", task="transcribe")
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# Load data
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ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "de", split="test", streaming=True)
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test_segment = next(iter(ds_mcv_test))
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waveform = test_segment["audio"]
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# NB: decoding option
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# limit the maximum number of generated tokens to 225
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pipe.model.config.max_length = 225 + 1
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# sampling
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# pipe.model.config.do_sample = True
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# beam search
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# pipe.model.config.num_beams = 5
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# return
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# pipe.model.config.return_dict_in_generate = True
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# pipe.model.config.output_scores = True
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# pipe.model.config.num_return_sequences = 5
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# Run
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generated_sentences = pipe(waveform)["text"]
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```
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Inference with 🤗 low-level APIs
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```python
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import torch
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import torchaudio
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from datasets import load_dataset
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from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Load model
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model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-small-cv11-german-punct").to(device)
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processor = AutoProcessor.from_pretrained("bofenghuang/whisper-small-cv11-german-punct", language="german", task="transcribe")
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# NB: set forced_decoder_ids for generation utils
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model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="de", task="transcribe")
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# 16_000
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model_sample_rate = processor.feature_extractor.sampling_rate
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# Load data
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ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "de", split="test", streaming=True)
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test_segment = next(iter(ds_mcv_test))
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waveform = torch.from_numpy(test_segment["audio"]["array"])
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sample_rate = test_segment["audio"]["sampling_rate"]
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# Resample
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if sample_rate != model_sample_rate:
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resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate)
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waveform = resampler(waveform)
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# Get feat
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inputs = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt")
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input_features = inputs.input_features
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input_features = input_features.to(device)
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# Generate
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generated_ids = model.generate(inputs=input_features, max_new_tokens=225) # greedy
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# generated_ids = model.generate(inputs=input_features, max_new_tokens=225, num_beams=5) # beam search
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# Detokenize
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generated_sentences = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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# Normalise predicted sentences if necessary
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```
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added_tokens.json
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{
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"<|so|>": 50326,
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"<|sq|>": 50317,
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"<|startoflm|>": 50360,
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"<|zh|>": 50260
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}
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config.json
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{
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"_name_or_path": "openai/whisper-small",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"architectures": [
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"WhisperForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"begin_suppress_tokens": [
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220,
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50257
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],
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"bos_token_id": 50257,
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"d_model": 768,
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"decoder_attention_heads": 12,
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"decoder_ffn_dim": 3072,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 12,
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"decoder_start_token_id": 50258,
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"dropout": 0.05,
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"encoder_attention_heads": 12,
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"encoder_ffn_dim": 3072,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 12,
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"eos_token_id": 50257,
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"forced_decoder_ids": null,
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"max_length": 448,
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"max_source_positions": 1500,
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"max_target_positions": 448,
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"model_type": "whisper",
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"num_hidden_layers": 12,
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"num_mel_bins": 80,
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"pad_token_id": 50257,
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"scale_embedding": false,
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"torch_dtype": "float32",
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"transformers_version": "4.26.0.dev0",
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"use_cache": false,
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"vocab_size": 51865
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}
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merges.txt
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See raw diff
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preprocessor_config.json
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pytorch_model.bin
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results_cv11_greedy/log_predictions.txt
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results_cv11_greedy/log_targets.txt
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vocab.json
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