decoding

游雁

2024-06-11 d43f77408b8f3e169c59dfb6b6d82e45e6b91714

 funasr/auto/auto_model.py

@@ -42,8 +42,9 @@
    filelist = [".scp", ".txt", ".json", ".jsonl", ".text"]

    chars = string.ascii_letters + string.digits
    if isinstance(data_in, str) and data_in.startswith("http"):  # url
        data_in = download_from_url(data_in)
    if isinstance(data_in, str):
        if data_in.startswith("http://") or data_in.startswith("https://"):  # url
            data_in = download_from_url(data_in)

    if isinstance(data_in, str) and os.path.exists(
        data_in
@@ -211,7 +212,6 @@
        deep_update(model_conf, kwargs.get("model_conf", {}))
        deep_update(model_conf, kwargs)
        model = model_class(**model_conf, vocab_size=vocab_size)
        model.to(device)

        # init_param
        init_param = kwargs.get("init_param", None)
@@ -232,6 +232,9 @@
        # fp16
        if kwargs.get("fp16", False):
            model.to(torch.float16)
        elif kwargs.get("bf16", False):
            model.to(torch.bfloat16)
        model.to(device)
        return model, kwargs

    def __call__(self, *args, **cfg):
@@ -284,7 +287,7 @@
            with torch.no_grad():
                res = model.inference(**batch, **kwargs)
                if isinstance(res, (list, tuple)):
                    results = res[0]
                    results = res[0] if len(res) > 0 else [{"text": ""}]
                    meta_data = res[1] if len(res) > 1 else {}
            time2 = time.perf_counter()

@@ -358,13 +361,13 @@
            results_sorted = []

            if not len(sorted_data):
                results_ret_list.append({"key": key, "text": "", "timestamp": []})
                logging.info("decoding, utt: {}, empty speech".format(key))
                continue

            if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
                batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])

            batch_size_ms_cum = 0
            beg_idx = 0
            beg_asr_total = time.time()
            time_speech_total_per_sample = speech_lengths / 16000
@@ -373,19 +376,22 @@
            # pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True)

            all_segments = []
            max_len_in_batch = 0
            end_idx = 1
            for j, _ in enumerate(range(0, n)):
                # pbar_sample.update(1)
                batch_size_ms_cum += sorted_data[j][0][1] - sorted_data[j][0][0]
                sample_length = sorted_data[j][0][1] - sorted_data[j][0][0]
                potential_batch_length = max(max_len_in_batch, sample_length) * (j + 1 - beg_idx)
                # batch_size_ms_cum += sorted_data[j][0][1] - sorted_data[j][0][0]
                if (
                    j < n - 1
                    and (batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0])
                    < batch_size
                    and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0])
                    < batch_size_threshold_ms
                    and sample_length < batch_size_threshold_ms
                    and potential_batch_length < batch_size
                ):
                    max_len_in_batch = max(max_len_in_batch, sample_length)
                    end_idx += 1
                    continue
                batch_size_ms_cum = 0
                end_idx = j + 1

                speech_j, speech_lengths_j = slice_padding_audio_samples(
                    speech, speech_lengths, sorted_data[beg_idx:end_idx]
                )
@@ -410,6 +416,8 @@
                        )
                        results[_b]["spk_embedding"] = spk_res[0]["spk_embedding"]
                beg_idx = end_idx
                end_idx += 1
                max_len_in_batch = sample_length
                if len(results) < 1:
                    continue
                results_sorted.extend(results)
@@ -421,6 +429,10 @@
            #                      f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
            #                      f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")

            if len(results_sorted) != n:
                results_ret_list.append({"key": key, "text": "", "timestamp": []})
                logging.info("decoding, utt: {}, empty result".format(key))
                continue
            restored_data = [0] * n
            for j in range(n):
                index = sorted_data[j][1]