Update app.py

app.py

@@ -11,148 +11,5 @@ import numpy as np
 auth_token = os.environ.get("key")
 os.environ["HUGGING_FACE_HUB_TOKEN"] = auth_token
 
-
-# set up transcription pipeline
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en", device=device)
-
-
-# set up translation pipeline
-translation_model_path =  "mutisya/m2m100_418M-en-kik-v24.03.2"
-
-def update_tokenizer_settings(tokenizer):
-    new_langTokens = { k: tokenizer.convert_tokens_to_ids(k) for k in tokenizer.additional_special_tokens }
-    all_lang_tokens =dict(list(tokenizer.lang_token_to_id.items())  + list(new_langTokens.items()))
-
-    tokenizer.id_to_lang_token = { v : k for k,v in all_lang_tokens.items() }
-    tokenizer.lang_token_to_id = { k : v for k,v in all_lang_tokens.items() }
-    tokenizer.lang_code_to_token = { k.replace("_", ""): k for k in all_lang_tokens.keys() }
-    tokenizer.lang_code_to_id = { k.replace("_", ""): v for k, v in all_lang_tokens.items() }
-
-
-translation_model = M2M100ForConditionalGeneration.from_pretrained(translation_model_path)
-translation_tokenizer = M2M100Tokenizer.from_pretrained(translation_model_path)
-
-update_tokenizer_settings(translation_tokenizer)
-
-# set translation direction
-src_lang = "en"
-tgt_lang = "kik"
-
-translation_tokenizer.src_lang = src_lang
-translation_tokenizer.tgt_lang = tgt_lang
-
-
-translation_device = 0 if torch.cuda.is_available() else -1
-translator = pipeline('translation', model=translation_model, tokenizer=translation_tokenizer, device=translation_device)
-
-
-# transcribe sections while keeping state
-chunk_tracker = []
-ready_to_translate = []
-text_at_chunk_end = ""
-chunk_index = 0;
-translated_text = ""
-transcribed_text = ""
-
-
-def get_next_translation_block():
-    global text_at_chunk_end
-    global chunk_tracker
-    global ready_to_translate
-    global translated_text
-    global transcribed_text
-
-    last_stop = text_at_chunk_end[0:-1].rfind('.')
-    ready_sentences = text_at_chunk_end[0:last_stop+1]
-    chunks_to_remove = []
-
-    if len(ready_sentences) > 0:
-      print("Trying to match: "+ ready_sentences)
-      found_match = False
-      for i in range(0, len(chunk_tracker)):
-        curr_chunk = chunk_tracker[i]
-        chunks_to_remove.append(curr_chunk)
-        if curr_chunk["text_at_begining"] == curr_chunk["text_at_end"] and curr_chunk["text_at_begining"] == ready_sentences:
-          found_match = True
-          break
-
-      if found_match == False:
-        print("ERROR: no match found for "+ ready_sentences)
-        chunks_to_remove = []
-      else:
-        transcribed_text += ready_sentences
-        translated_text += translator(ready_sentences, src_lang=src_lang,tgt_lang=tgt_lang)[0]['translation_text']
-        print("TRANSLATED: "+ translated_text)
-
-    return ready_sentences, chunks_to_remove
-
-def transcribe(stream, new_chunk):
-    global text_at_chunk_end
-    global chunk_tracker
-    global ready_to_translate
-    global chunk_index
-    global translated_text
-    global transcribed_text
-
-    chunk_index +=1
-
-    sr, y = new_chunk
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
-
-    chunk_value = y
-    chunk_length = len(y)
-
-    if stream is not None:
-        stream = np.concatenate([stream, y])
-    else:
-        stream = y
-    
-    text_at_chunk_begining = text_at_chunk_end
-    text_at_chunk_end = transcriber({"sampling_rate": sr, "raw": stream})["text"]
-
-    curr_chunk = {
-        "value": chunk_value,
-        "length": chunk_length,
-        "text_at_begining": text_at_chunk_begining,
-        "text_at_end": text_at_chunk_end
-    }
-
-    #print(curr_chunk)
-    chunk_tracker.append(curr_chunk)
-
-    # get translation block
-    if chunk_index % 5 == 0:
-      ready_sentences, chunks_to_remove = get_next_translation_block();
-      if len(chunks_to_remove) >0:
-        ready_to_translate.append(ready_sentences)
-        total_trim_length = 0
-        for i in range(0, len(chunks_to_remove)):
-          total_trim_length += chunks_to_remove[i]["length"]
-          removed = chunk_tracker.pop(0)
-          # print("REMOVED: "+ removed["text_at_begining"] +" -> " + removed["text_at_end"])
-
-        # set up new stream with remaining chunks
-        new_stream = chunk_tracker[0]["value"]
-        for i in range(1, len(chunk_tracker)):
-          new_stream = np.concatenate([new_stream, chunk_tracker[i]["value"]])
-
-        stream = new_stream
-
-    return stream, text_at_chunk_end, transcribed_text, translated_text
-
-
-# set up UI
-demo = gr.Interface(
-    transcribe,
-    ["state", gr.Audio(sources=["microphone"], streaming=True)],
-    ["state", gr.Textbox(label="in progress"), gr.Textbox(label="Transcribed text"), gr.Textbox(label="Translated text")],
-    live=True,
-    allow_flagging="never"
-)
-
-demo.dependencies[0]["show_progress"] = False  # this should hide the progress report?
-
-if __name__ == "__main__":
-    demo.launch(debug=True)
+iface = gr.load(name="mutisya/transcribe-api-feb24", hf_token=hf_token, src="spaces")
+iface.queue(api_open=False).launch(show_api=False)