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@@ -195,7 +195,7 @@ audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu()
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```py
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from IPython.display import Audio
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-sample_rate = model.sampling_rate
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+sample_rate = model.config.sampling_rate
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Audio(audio_array_from_text, rate=sample_rate)
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Audio(audio_array_from_audio, rate=sample_rate)
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```
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@@ -206,7 +206,7 @@ Or save them as a `.wav` file using a third-party library, e.g. `torchaudio`:
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torchaudio.save(
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<path_to_save_audio>,
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audio_array_from_audio, # or audio_array_from_text
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- sample_rate=model.sampling_rate,
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+ sample_rate=model.config.sampling_rate,
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)
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```
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2. (bis) To run inference for text generating tasks (T2TT, ASR or S2TT), it is recommended to use [dedicated models](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t_v2#1-use-dedicated-models). With that, only the required sub-modules will be loaded. For exmaple, text-to-text translation from English to Bulgarian, is performed as follows:
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Maha