Create app.py

app.py

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+import gradio as gr
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+import torch
+import librosa
+import json
+
+with open('ISO_codes.json', 'r') as file:
+    iso_codes = json.load(file)
+
+languages = list(iso_codes.keys())
+
+model_id = "facebook/mms-1b-all"
+processor = AutoProcessor.from_pretrained(model_id)
+model = Wav2Vec2ForCTC.from_pretrained(model_id)
+
+def transcribe(audio_file_mic=None, audio_file_upload=None, language="English (eng)"):
+    if audio_file_mic:
+        audio_file = audio_file_mic
+    elif audio_file_upload:
+        audio_file = audio_file_upload
+    else:
+        return "Please upload an audio file or record one"
+
+    # Make sure audio is 16kHz
+    speech, sample_rate = librosa.load(audio_file)
+    if sample_rate != 16000:
+        speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)
+
+    # Keep the same model in memory and simply switch out the language adapters by calling load_adapter() for the model and set_target_lang() for the tokenizer
+    language_code = iso_codes[language]
+    processor.tokenizer.set_target_lang(language_code)
+    model.load_adapter(language_code)
+
+    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs).logits
+
+    ids = torch.argmax(outputs, dim=-1)[0]
+    transcription = processor.decode(ids)
+    return transcription
+
+examples = [["kab_1.mp3", None, "Amazigh (kab)"],
+            ["kab_2.mp3", None, "Amazigh (kab)"]]
+
+description = '''Automatic Speech Recognition with [MMS](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) (Massively Multilingual Speech) by Meta.
+Supports [1162 languages](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). Read the paper for more details: [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516).'''
+
+iface = gr.Interface(fn=transcribe,
+                     inputs=[
+                         gr.Audio(source="microphone", type="filepath", label="Record Audio"),
+                         gr.Audio(source="upload", type="filepath", label="Upload Audio"),
+                         gr.Dropdown(choices=languages, label="Language", value="English (eng)")
+                         ],
+                     outputs=gr.Textbox(label="Transcription"),
+                     examples=examples,
+                     description=description
+                     )
+iface.launch()