added files

app.py

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+from transformers import AutoProcessor, AutoModelForCTC
+from transformers import pipeline
+
+import soundfile as sf
+import gradio as gr
+import librosa
+import torch
+import sox
+import os
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+api_token = os.getenv("API_TOKEN")
+asr_processor = AutoProcessor.from_pretrained("imvladikon/wav2vec2-xls-r-300m-hebrew")
+asr_model = AutoModelForCTC.from_pretrained("imvladikon/wav2vec2-xls-r-300m-hebrew")
+
+en_he_translator = pipeline("translation_en_to_he")
+
+
+def process_audio_file(file):
+    data, sr = librosa.load(file)
+    if sr != 16000:
+        data = librosa.resample(data, sr, 16000)
+        
+    input_values = processor(data, sampling_rate=16_000, return_tensors="pt").input_values #.to(device)
+    return input_values
+
+def transcribe(file_mic, file_upload):
+    warn_output = ""
+    if (file_mic is not None) and (file_upload is not None):
+       warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+       file = file_mic
+    elif (file_mic is None) and (file_upload is None):
+       return "ERROR: You have to either use the microphone or upload an audio file"
+    elif file_mic is not None:
+       file = file_mic
+    else:
+       file = file_upload
+       
+    input_values = process_audio_file(file)
+    logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    return warn_output + transcription
+
+def convert(inputfile, outfile):
+    sox_tfm = sox.Transformer()
+    sox_tfm.set_output_format(
+        file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16
+    )
+    sox_tfm.build(inputfile, outfile)
+
+def parse_transcription(wav_file):
+    filename = wav_file.name.split('.')[0]
+    convert(wav_file.name, filename + "16k.wav")
+    speech, _ = sf.read(filename + "16k.wav")
+    print(speech.shape)
+    input_values = trans_processor(speech, sampling_rate=16_000, return_tensors="pt").input_values
+    logits = trans_model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = trans_processor.decode(predicted_ids[0], skip_special_tokens=True)
+    translated = en_he_translator(trasncription)
+    return transcription
+
+output = gr.outputs.Textbox(label="TEXT")
+input_mic = gr.inputs.Audio(source="microphone", type="file", optional=True)
+input_upload = gr.inputs.Audio(source="upload", type="file", optional=True)
+
+gr.Interface(parse_transcription, inputs=[input_mic],  outputs=output,
+             analytics_enabled=False,
+             show_tips=False,
+             theme='huggingface',
+             layout='horizontal',
+             title="Draw Me A Ship in Hebrew",
+             enable_queue=True).launch(inline=False)

packages.txt

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+libsndfile1
+sox
+ffmpeg

requirements.txt

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+gradio
+librosa
+soundfile
+torch
+transformers
+sox
+sentencepiece