![]() The Common Voice train, validation datasets and Japanese speech corpus basic5000 datasets were used for training. format( 100 * wer.compute(predictions=result, references=result))) map(evaluate, batched= True, batch_size= 8) Logits = model(inputs.input_values.to( "cuda"), attention_mask=inputs.attention_mask.to( "cuda")).logitsīatch = processor.batch_decode(pred_ids) Inputs = processor(batch, sampling_rate= 16_000, return_tensors= "pt", padding= True) # evaluate function def evaluate( batch): !pip install mecab-python3įrom datasets import load_dataset, load_metric The model can be evaluated as follows on the Japanese test data of Common Voice. Print( "Prediction:", processor.batch_decode(predicted_ids)) All customers get 60 minutes for transcribing and analyzing audio free per month, not charged against your credits. New customers get 300 in free credits to spend on Speech-to-Text. A voice can also be set from one of the voices returned by () method. By default this set to the most suitable voice for the given language. By default this is set to browser language. Predicted_ids = torch.argmax(logits, dim= -1) Accurately convert speech into text with an API powered by the best of Google’s AI research and technology. text: Text of the speech lang: Language of the speech. Logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits You can use our voice for your work, as well as create your own videos that you can place on Facebook, YouTube, Vimeo, Instagram or personal websites. ![]() We have over 200 standard AI voices and natural human-like voices, in over 50 languages worldwide. ![]() Inputs = processor(test_dataset, sampling_rate= 16_000, return_tensors= "pt", padding= True) is a FREE online Text-to-Speech (TTS Free) website based on AI technology. Speech_array, sampling_rate = torchaudio.load(batch)īatch = resampler(sampling_rate, speech_array).squeeze() def speech_file_to_array_fn( batch):īatch = wakati.parse(batch).strip()īatch = re.sub(chars_to_ignore_regex, '', batch).strip() Resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000) Model = om_pretrained( "vumichien/wav2vec2-large-xlsr-japanese") Processor = om_pretrained( "vumichien/wav2vec2-large-xlsr-japanese") Test_dataset = load_dataset( "common_voice", "ja", split= "test") The model can be used directly (without a language model) as follows: !pip install mecab-python3įrom transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorĬhars_to_ignore_regex = '' # load data, processor and model When using this model, make sure that your speech input is sampled at 16kHz. TextToMp3 is text to speech(TTS) app that provides multi-lingual speech synthesis services, supports multiple voices of announcers and various types of background music. Give us a call at -tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice and Japanese speech corpus of Saruwatari-lab, University of Tokyo JSUT. To translate your audio, we first need to transcribe it. We currently support translating from Japanese to English, Spanish, French, German, Mandarin, Dutch, Portuguese, Russian, Italian, Japanese, and Polish. The JSUT corpus contains 10 hours of reading-style speech uttered. The first 10 minutes are free and theres no file limit. Text to Speech is also available to developers building their own applications, and APIs are available to integrate the module with third-party applications. They are designed mainly for text-to-speech synthesis and voice conversion, respectively. Text to Speech functionality can be incorporated into any Oddcast custom application.
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