Initial commit.

app.py

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+import gradio as gr
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+import torch
+import numpy as np
+import librosa
+
+model_id = "facebook/mms-1b-all"
+
+def transcribe(audio_file_mic=None, audio_file_upload=None):
+    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"
+
+    speech, sample_rate = librosa.load(audio_file)
+    if sample_rate != 16000:
+        speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)
+
+    processor = AutoProcessor.from_pretrained(model_id)
+    model = Wav2Vec2ForCTC.from_pretrained(model_id)
+
+    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
+
+iface = gr.Interface(fn=transcribe,
+                     inputs=[
+                         gr.Audio(source="microphone", type="filepath"),
+                         gr.Audio(source="upload", type="filepath")
+                         ],
+                     outputs=["textbox"],
+                     )
+iface.launch()

requirements.txt

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+git+https://github.com/huggingface/transformers.git
+torch
+librosa