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espnet/mediaspeech-fr-hubert
mediaspeech-fr-hubert is a automatic speech recognition model from espnet. Use it when you need speech turned into text. It is set up for espnet. The card lists the license as cc-by-4.0.
- date: Tue Mar 22 13:50:31 UTC 2022 - python version: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] - espnet version: espnet 0.10.7a1 - pytorch version: pytorch 1.10.1 - Git hash: 1991a25855821b8b61d775681aa0cd…
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Updated Sep 19, 2026
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From the Hugging Face model README
import librosa
from espnet2.bin.asr_inference import Speech2Text
speech2text = Speech2Text.from_pretrained(model_tag="espnet/mediaspeech-fr-hubert")
# librosa resamples and mixes to one channel, so any file works; 16000 is
# what nearly every espnet recogniser is trained on - check this model's
# config if its audio is not 16 kHz
speech, rate = librosa.load("audio.wav", sr=16000, mono=True)
text, *_ = speech2text(speech)[0]
print(text)
<!-- Generated by scripts/utils/show_asr_result.sh -->
Tue Mar 22 13:50:31 UTC 20223.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]espnet 0.10.7a1pytorch 1.10.11991a25855821b8b61d775681aa0cdfd6161bbc8
Mon Mar 21 22:19:19 2022 +0800| dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
|---|---|---|---|---|---|---|---|---|
| inference_asr_model_valid.acc.ave/dev_as | 249 | 10072 | 49.7 | 41.2 | 9.1 | 7.0 | 57.2 | 100.0 |
| inference_asr_model_valid.acc.ave/test_as | 249 | 9920 | 51.1 | 40.1 | 8.9 | 6.5 | 55.4 | 100.0 |
| dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
|---|---|---|---|---|---|---|---|---|
| inference_asr_model_valid.acc.ave/dev_as | 249 | 58679 | 80.9 | 8.0 | 11.1 | 7.2 | 26.3 | 100.0 |
| inference_asr_model_valid.acc.ave/test_as | 249 | 58694 | 82.1 | 7.2 | 10.8 | 7.1 | 25.0 | 100.0 |
| dataset | Snt | Wrd | Corr | Sub | Del | Ins | Err | S.Err |
|---|---|---|---|---|---|---|---|---|
| inference_asr_model_valid.acc.ave/dev_as | 249 | 30837 | 69.5 | 19.0 | 11.5 | 6.3 | 36.8 | 100.0 |
| inference_asr_model_valid.acc.ave/test_as | 249 | 30942 | 70.7 | 17.9 | 11.4 | 6.0 | 35.3 | 100.0 |