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--- |
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license: apache-2.0 |
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language: fr |
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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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- hf-asr-leaderboard |
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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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- facebook/multilingual_librispeech |
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- facebook/voxpopuli |
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- google/fleurs |
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- gigant/african_accented_french |
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metrics: |
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- wer |
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model-index: |
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- name: Fine-tuned whisper-medium model for ASR in French |
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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: fr |
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split: test |
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args: fr |
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metrics: |
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- name: WER (Greedy) |
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type: wer |
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value: 9.03 |
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- name: WER (Beam 5) |
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type: wer |
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value: 8.73 |
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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: Multilingual LibriSpeech (MLS) |
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type: facebook/multilingual_librispeech |
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config: french |
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split: test |
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args: french |
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metrics: |
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- name: WER (Greedy) |
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type: wer |
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value: 4.60 |
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- name: WER (Beam 5) |
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type: wer |
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value: 4.44 |
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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: VoxPopuli |
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type: facebook/voxpopuli |
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config: fr |
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split: test |
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args: fr |
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metrics: |
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- name: WER (Greedy) |
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type: wer |
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value: 9.53 |
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- name: WER (Beam 5) |
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type: wer |
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value: 9.46 |
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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: Fleurs |
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type: google/fleurs |
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config: fr_fr |
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split: test |
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args: fr_fr |
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metrics: |
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- name: WER (Greedy) |
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type: wer |
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value: 6.33 |
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- name: WER (Beam 5) |
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type: wer |
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value: 5.94 |
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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: African Accented French |
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type: gigant/african_accented_french |
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config: fr |
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split: test |
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args: fr |
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metrics: |
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- name: WER (Greedy) |
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type: wer |
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value: 4.89 |
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- name: WER (Beam 5) |
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type: wer |
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value: 4.56 |
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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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# Fine-tuned whisper-medium model for ASR in French |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co./openai/whisper-medium), trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and the validation splits of [Common Voice 11.0](https://huggingface.co./datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co./datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Fleurs](https://huggingface.co./datasets/google/fleurs), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co./datasets/gigant/african_accented_french). When using the model make sure that your speech input is sampled at 16Khz. **This model doesn't predict casing or punctuation.** |
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## Performance |
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*Below are the WERs of the pre-trained models on the [Common Voice 9.0](https://huggingface.co./datasets/mozilla-foundation/common_voice_9_0), [Multilingual LibriSpeech](https://huggingface.co./datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli) and [Fleurs](https://huggingface.co./datasets/google/fleurs). These results are reported in the original [paper](https://cdn.openai.com/papers/whisper.pdf).* |
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| Model | Common Voice 9.0 | MLS | VoxPopuli | Fleurs | |
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| --- | :---: | :---: | :---: | :---: | |
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| [openai/whisper-small](https://huggingface.co./openai/whisper-small) | 22.7 | 16.2 | 15.7 | 15.0 | |
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| [openai/whisper-medium](https://huggingface.co./openai/whisper-medium) | 16.0 | 8.9 | 12.2 | 8.7 | |
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| [openai/whisper-large](https://huggingface.co./openai/whisper-large) | 14.7 | 8.9 | **11.0** | **7.7** | |
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| [openai/whisper-large-v2](https://huggingface.co./openai/whisper-large-v2) | **13.9** | **7.3** | 11.4 | 8.3 | |
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*Below are the WERs of the fine-tuned models on the [Common Voice 11.0](https://huggingface.co./datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co./datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), and [Fleurs](https://huggingface.co./datasets/google/fleurs). Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of apostrophe. The results in the table are reported as `WER (greedy search) / WER (beam search with beam width 5)`.* |
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| Model | Common Voice 11.0 | MLS | VoxPopuli | Fleurs | |
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| --- | :---: | :---: | :---: | :---: | |
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| [bofenghuang/whisper-small-cv11-french](https://huggingface.co./bofenghuang/whisper-small-cv11-french) | 11.76 / 10.99 | 9.65 / 8.91 | 14.45 / 13.66 | 10.76 / 9.83 | |
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| [bofenghuang/whisper-medium-cv11-french](https://huggingface.co./bofenghuang/whisper-medium-cv11-french) | 9.03 / 8.54 | 6.34 / 5.86 | 11.64 / 11.35 | 7.13 / 6.85 | |
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| [bofenghuang/whisper-medium-french](https://huggingface.co./bofenghuang/whisper-medium-french) | 9.03 / 8.73 | 4.60 / 4.44 | 9.53 / 9.46 | 6.33 / 5.94 | |
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| [bofenghuang/whisper-large-v2-cv11-french](https://huggingface.co./bofenghuang/whisper-large-v2-cv11-french) | **8.05** / **7.67** | 5.56 / 5.28 | 11.50 / 10.69 | 5.42 / 5.05 | |
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| [bofenghuang/whisper-large-v2-french](https://huggingface.co./bofenghuang/whisper-large-v2-french) | 8.15 / 7.83 | **4.20** / **4.03** | **9.10** / **8.66** | **5.22** / **4.98** | |
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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-medium-french", 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="fr", task="transcribe") |
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# Load data |
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ds_mcv_test = load_dataset("mozilla-foundation/common_voice_11_0", "fr", 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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# Run |
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generated_sentences = pipe(waveform, max_new_tokens=225)["text"] # greedy |
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# generated_sentences = pipe(waveform, max_new_tokens=225, generate_kwargs={"num_beams": 5})["text"] # beam search |
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# Normalise predicted sentences if necessary |
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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-medium-french").to(device) |
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processor = AutoProcessor.from_pretrained("bofenghuang/whisper-medium-french", language="french", 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="fr", 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", "fr", 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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``` |
