Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. It is a distilled version of the Whisper model that is **6 times faster**, 49% smaller, and performs **within 1% WER** on out-of-distribution evaluation sets. This is the repository for distil-small.en, a distilled variant of Whisper small.en. It is the **smallest Distil-Whisper checkpoint**, with just 166M parameters, making it the ideal choice for memory constrained applications (e.g. on-device). For most other applications, the distil-medium.en or distil-large-v2 checkpoints are recommended, since they are both faster and achieve better WER results:
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"transcribe-translate" models provide signatures for "serving_transcribe" and "serving_translate" to force the model to perform a certain action @tf.function( input_signature=[ tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"), ], ) def transcribe(self, input_features): outputs = self.model.generate( input_features, max_new_tokens=450, # change as needed return_dict_in_generate=True, forced_decoder_ids=[[2, 50359], [3, 50363]], # forced to transcribe any language with no timestamps ) return {"sequences": outputs["sequences"]} @tf.function( input_signature=[ tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"), ], ) def translate(self, input_features): outputs = self.model.generate( input_features, max_new_tokens=450, # change as needed return_dict_in_generate=True, forced_decoder_ids=[[2, 50358], [3, 50363]], # forced to translate any language with no timestamps ) return {"sequences": outputs["sequences"]}
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Fine-tuned dragonSwing/wav2vec2-base-pretrain-vietnamese on Vietnamese Speech Recognition task using 100h labelled data from VSLP dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows:
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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.3421 - Wer: 0.2860 python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1
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This model is a fine-tuned version of openai/whisper-base.en on the Azure-dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0636 - Wer: 25.2804 The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - training_steps: 1000
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This model is a fine-tuned version of openai/whisper-tiny.en on the Azure-dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.0680 - Wer: 44.3486 The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - training_steps: 1000
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`Audio8-ASR-0.1B` is a compact autoregressive ASR model whose language-model component has only 0.1B parameters. It supports multilingual speech recognition for languages including Chinese, English, French, German, Japanese, Korean, and Cantonese. We position it as one of the smallest usable performance ASR models in the LLM era. This base repository provides the Hugging Face Transformers checkpoint. We also provide deployment-focused releases: - Audio8-ASR-0.1B-onnx-runtime - Audio8-ASR-0.1B-iOS-ANE
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**Audio8 ASR Infinite** is a native streaming speech recognition model built to be as responsive as possible. It offers a selectable audio clock (80/120/160 ms) and a transcription delay (240–560 ms). With our adapted vLLM build it transcribes unlimited-length audio **24/7** without drifting. - **Super responsive** — the native streaming architecture decodes 12.5 times per second. - **Unlimited-length transcription** — a rolling KV Cache keeps both **memory and latency constant**, even in **24/7 operation**. - **Selectable streaming clock** — one text token per clock step (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource cost. - **Configurable transcription delay** — set how much delay to trade for accuracy. - **Semantic VAD** — distinguishes thinking pauses, stuttering and real end of turn, where traditional acoustic VAD usually fails. - **Bilingual** — Chinese and English. The checkpoint has a native context of 30 seconds. But with Rolling KV Cache, it can transcribe 24/7 nonstop.
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Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details.
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Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details.
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Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details. The paper is also available on Hugging Face: Link to Hugging Face Paper Page
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Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our GitHub repository and paper for details. The easiest way to run our model is to use our integration with HuggingFace Transformers library. We provide model weights for the compressed version of OpenAI Whisper series here.
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Arabic using the `train` splits of Common Voice and Arabic Speech Corpus. When using this model, make sure that your speech input is sampled at 16kHz. For more details, see Fine-Tuning with Arabic Speech Corpus. This model represents Arabic in a format called Buckwalter transliteration. The Buckwalter format only includes ASCII characters, some of which are non-alpha (e.g., `">"` maps to `"أ"`). The lang-trans package is used to convert (transliterate) Arabic abjad.
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